FYS-STK4155/doc/LectureNotes/_build/jupyter_execute/chapter6.ipynb

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    "# Decision trees, overarching aims\n",
    "\n",
    "\n",
    "We start here with the most basic algorithm, the so-called decision\n",
    "tree. With this basic algorithm we can in turn build more complex\n",
    "networks, spanning from homogeneous and heterogenous forests (bagging,\n",
    "random forests and more) to one of the most popular supervised\n",
    "algorithms nowadays, the extreme gradient boosting, or just\n",
    "XGBoost. But let us start with the simplest possible ingredient.\n",
    "\n",
    "Decision trees are supervised learning algorithms used for both,\n",
    "classification and regression tasks.\n",
    "\n",
    "\n",
    "The main idea of decision trees\n",
    "is to find those descriptive features which contain the most\n",
    "**information** regarding the target feature and then split the dataset\n",
    "along the values of these features such that the target feature values\n",
    "for the resulting underlying datasets are as pure as possible.\n",
    "\n",
    "The descriptive features which reproduce best the target/output features are normally  said\n",
    "to be the most informative ones. The process of finding the **most\n",
    "informative** feature is done until we accomplish a stopping criteria\n",
    "where we then finally end up in so called **leaf nodes**. \n",
    "\n",
    "## Basics of a tree\n",
    "\n",
    "A decision tree is typically divided into a **root node**, the **interior nodes**,\n",
    "and the final **leaf nodes** or just **leaves**. These entities are then connected by so-called **branches**.\n",
    "\n",
    "The leaf nodes\n",
    "contain the predictions we will make for new query instances presented\n",
    "to our trained model. This is possible since the model has \n",
    "learned the underlying structure of the training data and hence can,\n",
    "given some assumptions, make predictions about the target feature value\n",
    "(class) of unseen query instances.\n",
    "\n",
    "\n",
    "## General Features\n",
    "\n",
    "The overarching approach to decision trees is a top-down approach.\n",
    "\n",
    "* A leaf provides the classification of a given instance.\n",
    "\n",
    "* A node specifies a test of some attribute of the instance.\n",
    "\n",
    "* A branch corresponds to a possible values of an attribute.\n",
    "\n",
    "* An instance is classified by starting at the root node of the tree, testing the attribute specified by this node, then moving down the tree branch corresponding to the value of the attribute in the given example.\n",
    "\n",
    "This process is then repeated for the subtree rooted at the new\n",
    "node.\n",
    "\n",
    "\n",
    "\n",
    "In simplified terms, the process of training a decision tree and\n",
    "predicting the target features of query instances is as follows:\n",
    "\n",
    "1. Present a dataset containing of a number of training instances characterized by a number of descriptive features and a target feature\n",
    "\n",
    "2. Train the decision tree model by continuously splitting the target feature along the values of the descriptive features using a measure of information gain during the training process\n",
    "\n",
    "3. Grow the tree until we accomplish a stopping criteria create leaf nodes which represent the *predictions* we want to make for new query instances\n",
    "\n",
    "4. Show query instances to the tree and run down the tree until we arrive at leaf nodes\n",
    "\n",
    "Then we are essentially done!"
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      "2nd degree coefficients:\n",
      "zero power:  -0.7397605907501061\n",
      "first power:  0.007373805280423706\n",
      "second power:  0.00026005429911763394\n"
     ]
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "filenames": {
       "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapter6_1_1.png"
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "filenames": {
       "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapter6_1_2.png"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "steps=250\n",
    "\n",
    "distance=0\n",
    "x=0\n",
    "distance_list=[]\n",
    "steps_list=[]\n",
    "while x<steps:\n",
    "    distance+=np.random.randint(-1,2)\n",
    "    distance_list.append(distance)\n",
    "    x+=1\n",
    "    steps_list.append(x)\n",
    "plt.plot(steps_list,distance_list, color='green', label=\"Random Walk Data\")\n",
    "\n",
    "steps_list=np.asarray(steps_list)\n",
    "distance_list=np.asarray(distance_list)\n",
    "\n",
    "X=steps_list[:,np.newaxis]\n",
    "\n",
    "#Polynomial fits\n",
    "\n",
    "#Degree 2\n",
    "poly_features=PolynomialFeatures(degree=2, include_bias=False)\n",
    "X_poly=poly_features.fit_transform(X)\n",
    "\n",
    "lin_reg=LinearRegression()\n",
    "poly_fit=lin_reg.fit(X_poly,distance_list)\n",
    "b=lin_reg.coef_\n",
    "c=lin_reg.intercept_\n",
    "print (\"2nd degree coefficients:\")\n",
    "print (\"zero power: \",c)\n",
    "print (\"first power: \", b[0])\n",
    "print (\"second power: \",b[1])\n",
    "\n",
    "z = np.arange(0, steps, .01)\n",
    "z_mod=b[1]*z**2+b[0]*z+c\n",
    "\n",
    "fit_mod=b[1]*X**2+b[0]*X+c\n",
    "plt.plot(z, z_mod, color='r', label=\"2nd Degree Fit\")\n",
    "plt.title(\"Polynomial Regression\")\n",
    "\n",
    "plt.xlabel(\"Steps\")\n",
    "plt.ylabel(\"Distance\")\n",
    "\n",
    "#Degree 10\n",
    "poly_features10=PolynomialFeatures(degree=10, include_bias=False)\n",
    "X_poly10=poly_features10.fit_transform(X)\n",
    "\n",
    "poly_fit10=lin_reg.fit(X_poly10,distance_list)\n",
    "\n",
    "y_plot=poly_fit10.predict(X_poly10)\n",
    "plt.plot(X, y_plot, color='black', label=\"10th Degree Fit\")\n",
    "\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "\n",
    "#Decision Tree Regression\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "regr_1=DecisionTreeRegressor(max_depth=2)\n",
    "regr_2=DecisionTreeRegressor(max_depth=5)\n",
    "regr_3=DecisionTreeRegressor(max_depth=7)\n",
    "regr_1.fit(X, distance_list)\n",
    "regr_2.fit(X, distance_list)\n",
    "regr_3.fit(X, distance_list)\n",
    "\n",
    "X_test = np.arange(0.0, steps, 0.01)[:, np.newaxis]\n",
    "y_1 = regr_1.predict(X_test)\n",
    "y_2 = regr_2.predict(X_test)\n",
    "y_3=regr_3.predict(X_test)\n",
    "\n",
    "# Plot the results\n",
    "plt.figure()\n",
    "plt.scatter(X, distance_list, s=2.5, c=\"black\", label=\"data\")\n",
    "plt.plot(X_test, y_1, color=\"red\",\n",
    "         label=\"max_depth=2\", linewidth=2)\n",
    "plt.plot(X_test, y_2, color=\"green\", label=\"max_depth=5\", linewidth=2)\n",
    "plt.plot(X_test, y_3, color=\"m\", label=\"max_depth=7\", linewidth=2)\n",
    "\n",
    "plt.xlabel(\"Data\")\n",
    "plt.ylabel(\"Darget\")\n",
    "plt.title(\"Decision Tree Regression\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Building a tree, regression\n",
    "\n",
    "There are mainly two steps\n",
    "1. We split the predictor space (the set of possible values $x_1,x_2,\\dots, x_p$) into $J$ distinct and non-non-overlapping regions, $R_1,R_2,\\dots,R_J$.  \n",
    "\n",
    "2. For every observation that falls into the region $R_j$ , we make the same prediction, which is simply the mean of the response values for the training observations in $R_j$.\n",
    "\n",
    "How do we construct the regions $R_1,\\dots,R_J$?  In theory, the\n",
    "regions could have any shape. However, we choose to divide the\n",
    "predictor space into high-dimensional rectangles, or boxes, for\n",
    "simplicity and for ease of interpretation of the resulting predictive\n",
    "model. The goal is to find boxes $R_1,\\dots,R_J$ that minimize the\n",
    "MSE, given by"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$\n",
    "\\sum_{j=1}^J\\sum_{i\\in R_j}(y_i-\\overline{y}_{R_j})^2,\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "where $\\overline{y}_{R_j}$  is the mean response for the training observations \n",
    "within box $j$. \n",
    "\n",
    "\n",
    "Unfortunately, it is computationally infeasible to consider every\n",
    "possible partition of the feature space into $J$ boxes.  The common\n",
    "strategy is to take a top-down approach\n",
    "\n",
    "The approach is top-down because it begins at the top of the tree (all\n",
    "observations belong to a single region) and then successively splits\n",
    "the predictor space; each split is indicated via two new branches\n",
    "further down on the tree. It is greedy because at each step of the\n",
    "tree-building process, the best split is made at that particular step,\n",
    "rather than looking ahead and picking a split that will lead to a\n",
    "better tree in some future step.\n",
    "\n",
    "\n",
    "### Making a tree\n",
    "\n",
    "In order to implement the recursive binary splitting we start by selecting\n",
    "the predictor $x_j$ and a cutpoint $s$ that splits the predictor space into two regions $R_1$ and $R_2$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$\n",
    "\\left\\{X\\vert x_j < s\\right\\},\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "and"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$\n",
    "\\left\\{X\\vert x_j \\geq s\\right\\},\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "so that we obtain the lowest MSE, that is"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$\n",
    "\\sum_{i:x_i\\in R_j}(y_i-\\overline{y}_{R_1})^2+\\sum_{i:x_i\\in R_2}(y_i-\\overline{y}_{R_2})^2,\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "which we want to minimize by considering all predictors\n",
    "$x_1,x_2,\\dots,x_p$.  We consider also all possible values of $s$ for\n",
    "each predictor. These values could be determined by randomly assigned\n",
    "numbers or by starting at the midpoint and then proceed till we find\n",
    "an optimal value.\n",
    "\n",
    "For any $j$ and $s$, we define the pair of half-planes where\n",
    "$\\overline{y}_{R_1}$ is the mean response for the training\n",
    "observations in $R_1(j,s)$, and $\\overline{y}_{R_2}$ is the mean\n",
    "response for the training observations in $R_2(j,s)$.\n",
    "\n",
    "Finding the values of $j$ and $s$ that minimize the above equation can be\n",
    "done quite quickly, especially when the number of features $p$ is not\n",
    "too large.\n",
    "\n",
    "Next, we repeat the process, looking\n",
    "for the best predictor and best cutpoint in order to split the data\n",
    "further so as to minimize the MSE within each of the resulting\n",
    "regions. However, this time, instead of splitting the entire predictor\n",
    "space, we split one of the two previously identified regions. We now\n",
    "have three regions. Again, we look to split one of these three regions\n",
    "further, so as to minimize the MSE. The process continues until a\n",
    "stopping criterion is reached; for instance, we may continue until no\n",
    "region contains more than five observations.\n",
    "\n",
    "\n",
    "The above procedure is rather straightforward, but leads often to\n",
    "overfitting and unnecessarily large and complicated trees. The basic\n",
    "idea is to grow a large tree $T_0$ and then prune it back in order to\n",
    "obtain a subtree. A smaller tree with fewer splits (fewer regions) can\n",
    "lead to smaller variance and better interpretation at the cost of a\n",
    "little more bias.\n",
    "\n",
    "The so-called Cost complexity pruning algorithm gives us a\n",
    "way to do just this. Rather than considering every possible subtree,\n",
    "we consider a sequence of trees indexed by a nonnegative tuning\n",
    "parameter $\\alpha$.\n",
    "\n",
    "Read more at the following [Scikit-Learn link on pruning](https://scikit-learn.org/stable/auto_examples/tree/plot_cost_complexity_pruning.html#sphx-glr-auto-examples-tree-plot-cost-complexity-pruning-py).\n",
    "\n",
    "\n",
    "For each value of $\\alpha$  there corresponds a subtree $T \\in T_0$ such that"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$\n",
    "\\sum_{m=1}^{\\overline{T}}\\sum_{i:x_i\\in R_m}(y_i-\\overline{y}_{R_m})^2+\\alpha\\overline{T},\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "is as small as possible. Here $\\overline{T}$ is \n",
    "the number of terminal nodes of the tree $T$ , $R_m$ is the\n",
    "rectangle (i.e. the subset of predictor space)  corresponding to the $m$-th terminal node.\n",
    "\n",
    "The tuning parameter $\\alpha$ controls a trade-off between the subtree’s\n",
    "complexity and its fit to the training data. When $\\alpha = 0$, then the\n",
    "subtree $T$ will simply equal $T_0$, \n",
    "because then the above equation just measures the\n",
    "training error. \n",
    "However, as $\\alpha$ increases, there is a price to pay for\n",
    "having a tree with many terminal nodes. The above equation will\n",
    "tend to be minimized for a smaller subtree. \n",
    "\n",
    "\n",
    "It turns out that as we increase $\\alpha$ from zero\n",
    "branches get pruned from the tree in a nested and predictable fashion,\n",
    "so obtaining the whole sequence of subtrees as a function of $\\alpha$ is\n",
    "easy. We can select a value of $\\alpha$ using a validation set or using\n",
    "cross-validation. We then return to the full data set and obtain the\n",
    "subtree corresponding to $\\alpha$. \n",
    "\n",
    "\n",
    "### Schematic Regression Procedure\n",
    "\n",
    "Building a Regression Tree\n",
    "\n",
    "1. Use recursive binary splitting to grow a large tree on the training data, stopping only when each terminal node has fewer than some minimum number of observations.\n",
    "\n",
    "2. Apply cost complexity pruning to the large tree in order to obtain a sequence of best subtrees, as a function of $\\alpha$.\n",
    "\n",
    "3. Use for example $K$-fold cross-validation to choose $\\alpha$. Divide the training observations into $K$ folds. For each $k=1,2,\\dots,K$ we: \n",
    "\n",
    "  * repeat steps 1 and 2 on all but the $k$-th fold of the training data. \n",
    "\n",
    "  * Then we valuate the mean squared prediction error on the data in the left-out $k$-th fold, as a function of $\\alpha$.\n",
    "\n",
    "  * Finally  we average the results for each value of $\\alpha$, and pick $\\alpha$ to minimize the average error.\n",
    "\n",
    "\n",
    "4. Return the subtree from Step 2 that corresponds to the chosen value of $\\alpha$. \n",
    "\n",
    "!eblock\n",
    "\n",
    "\n",
    "\n",
    "## A Classification Tree\n",
    "\n",
    "A classification tree is very similar to a regression tree, except\n",
    "that it is used to predict a qualitative response rather than a\n",
    "quantitative one. Recall that for a regression tree, the predicted\n",
    "response for an observation is given by the mean response of the\n",
    "training observations that belong to the same terminal node. In\n",
    "contrast, for a classification tree, we predict that each observation\n",
    "belongs to the most commonly occurring class of training observations\n",
    "in the region to which it belongs. In interpreting the results of a\n",
    "classification tree, we are often interested not only in the class\n",
    "prediction corresponding to a particular terminal node region, but\n",
    "also in the class proportions among the training observations that\n",
    "fall into that region.  \n",
    "\n",
    "\n",
    "\n",
    "The task of growing a\n",
    "classification tree is quite similar to the task of growing a\n",
    "regression tree. Just as in the regression setting, we use recursive\n",
    "binary splitting to grow a classification tree. However, in the\n",
    "classification setting, the MSE cannot be used as a criterion for making\n",
    "the binary splits.  A natural alternative to MSE is the **classification\n",
    "error rate**. Since we plan to assign an observation in a given region\n",
    "to the most commonly occurring error rate class of training\n",
    "observations in that region, the classification error rate is simply\n",
    "the fraction of the training observations in that region that do not\n",
    "belong to the most common class. \n",
    "\n",
    "When building a classification tree, either the Gini index or the\n",
    "entropy are typically used to evaluate the quality of a particular\n",
    "split, since these two approaches are more sensitive to node purity\n",
    "than is the classification error rate. \n",
    "\n",
    "\n",
    "\n",
    "If our targets are the outcome of a classification process that takes\n",
    "for example $k=1,2,\\dots,K$ values, the only thing we need to think of\n",
    "is to set up the splitting criteria for each node.\n",
    "\n",
    "We define a PDF $p_{mk}$ that represents the number of observations of\n",
    "a class $k$ in a region $R_m$ with $N_m$ observations. We represent\n",
    "this likelihood function in terms of the proportion $I(y_i=k)$ of\n",
    "observations of this class in the region $R_m$ as"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$\n",
    "p_{mk} = \\frac{1}{N_m}\\sum_{x_i\\in R_m}I(y_i=k).\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We let $p_{mk}$ represent the majority class of observations in region\n",
    "$m$. The three most common ways of splitting a node are given by\n",
    "\n",
    "* Misclassification error"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$\n",
    "p_{mk} = \\frac{1}{N_m}\\sum_{x_i\\in R_m}I(y_i\\ne k) = 1-p_{mk}.\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Gini index $g$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$\n",
    "g = \\sum_{k=1}^K p_{mk}(1-p_{mk}).\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Information entropy or just entropy $s$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$\n",
    "s = -\\sum_{k=1}^K p_{mk}\\log{p_{mk}}.\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualizing the Tree, Classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     mean radius  mean texture  mean perimeter  mean area  mean smoothness  \\\n",
      "0          17.99         10.38          122.80     1001.0          0.11840   \n",
      "1          20.57         17.77          132.90     1326.0          0.08474   \n",
      "2          19.69         21.25          130.00     1203.0          0.10960   \n",
      "3          11.42         20.38           77.58      386.1          0.14250   \n",
      "4          20.29         14.34          135.10     1297.0          0.10030   \n",
      "..           ...           ...             ...        ...              ...   \n",
      "564        21.56         22.39          142.00     1479.0          0.11100   \n",
      "565        20.13         28.25          131.20     1261.0          0.09780   \n",
      "566        16.60         28.08          108.30      858.1          0.08455   \n",
      "567        20.60         29.33          140.10     1265.0          0.11780   \n",
      "568         7.76         24.54           47.92      181.0          0.05263   \n",
      "\n",
      "     mean compactness  mean concavity  mean concave points  mean symmetry  \\\n",
      "0             0.27760         0.30010              0.14710         0.2419   \n",
      "1             0.07864         0.08690              0.07017         0.1812   \n",
      "2             0.15990         0.19740              0.12790         0.2069   \n",
      "3             0.28390         0.24140              0.10520         0.2597   \n",
      "4             0.13280         0.19800              0.10430         0.1809   \n",
      "..                ...             ...                  ...            ...   \n",
      "564           0.11590         0.24390              0.13890         0.1726   \n",
      "565           0.10340         0.14400              0.09791         0.1752   \n",
      "566           0.10230         0.09251              0.05302         0.1590   \n",
      "567           0.27700         0.35140              0.15200         0.2397   \n",
      "568           0.04362         0.00000              0.00000         0.1587   \n",
      "\n",
      "     mean fractal dimension  ...  worst radius  worst texture  \\\n",
      "0                   0.07871  ...        25.380          17.33   \n",
      "1                   0.05667  ...        24.990          23.41   \n",
      "2                   0.05999  ...        23.570          25.53   \n",
      "3                   0.09744  ...        14.910          26.50   \n",
      "4                   0.05883  ...        22.540          16.67   \n",
      "..                      ...  ...           ...            ...   \n",
      "564                 0.05623  ...        25.450          26.40   \n",
      "565                 0.05533  ...        23.690          38.25   \n",
      "566                 0.05648  ...        18.980          34.12   \n",
      "567                 0.07016  ...        25.740          39.42   \n",
      "568                 0.05884  ...         9.456          30.37   \n",
      "\n",
      "     worst perimeter  worst area  worst smoothness  worst compactness  \\\n",
      "0             184.60      2019.0           0.16220            0.66560   \n",
      "1             158.80      1956.0           0.12380            0.18660   \n",
      "2             152.50      1709.0           0.14440            0.42450   \n",
      "3              98.87       567.7           0.20980            0.86630   \n",
      "4             152.20      1575.0           0.13740            0.20500   \n",
      "..               ...         ...               ...                ...   \n",
      "564           166.10      2027.0           0.14100            0.21130   \n",
      "565           155.00      1731.0           0.11660            0.19220   \n",
      "566           126.70      1124.0           0.11390            0.30940   \n",
      "567           184.60      1821.0           0.16500            0.86810   \n",
      "568            59.16       268.6           0.08996            0.06444   \n",
      "\n",
      "     worst concavity  worst concave points  worst symmetry  \\\n",
      "0             0.7119                0.2654          0.4601   \n",
      "1             0.2416                0.1860          0.2750   \n",
      "2             0.4504                0.2430          0.3613   \n",
      "3             0.6869                0.2575          0.6638   \n",
      "4             0.4000                0.1625          0.2364   \n",
      "..               ...                   ...             ...   \n",
      "564           0.4107                0.2216          0.2060   \n",
      "565           0.3215                0.1628          0.2572   \n",
      "566           0.3403                0.1418          0.2218   \n",
      "567           0.9387                0.2650          0.4087   \n",
      "568           0.0000                0.0000          0.2871   \n",
      "\n",
      "     worst fractal dimension  \n",
      "0                    0.11890  \n",
      "1                    0.08902  \n",
      "2                    0.08758  \n",
      "3                    0.17300  \n",
      "4                    0.07678  \n",
      "..                       ...  \n",
      "564                  0.07115  \n",
      "565                  0.06637  \n",
      "566                  0.07820  \n",
      "567                  0.12400  \n",
      "568                  0.07039  \n",
      "\n",
      "[569 rows x 30 columns]\n",
      "     malignant  benign\n",
      "0         True   False\n",
      "1         True   False\n",
      "2         True   False\n",
      "3         True   False\n",
      "4         True   False\n",
      "..         ...     ...\n",
      "564       True   False\n",
      "565       True   False\n",
      "566       True   False\n",
      "567       True   False\n",
      "568      False    True\n",
      "\n",
      "[569 rows x 2 columns]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "from sklearn.datasets import load_breast_cancer\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.tree import export_graphviz\n",
    "\n",
    "from IPython.display import Image \n",
    "from pydot import graph_from_dot_data\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "cancer = load_breast_cancer()\n",
    "X = pd.DataFrame(cancer.data, columns=cancer.feature_names)\n",
    "print(X)\n",
    "y = pd.Categorical.from_codes(cancer.target, cancer.target_names)\n",
    "y = pd.get_dummies(y)\n",
    "print(y)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=1)\n",
    "tree_clf = DecisionTreeClassifier(max_depth=5)\n",
    "tree_clf.fit(X_train, y_train)\n",
    "\n",
    "export_graphviz(\n",
    "    tree_clf,\n",
    "    out_file=\"DataFiles/cancer.dot\",\n",
    "    feature_names=cancer.feature_names,\n",
    "    class_names=cancer.target_names,\n",
    "    rounded=True,\n",
    "    filled=True\n",
    ")\n",
    "cmd = 'dot -Tpng DataFiles/cancer.dot -o DataFiles/cancer.png'\n",
    "os.system(cmd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Common imports\n",
    "import numpy as np\n",
    "from sklearn.model_selection import  train_test_split \n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.datasets import make_moons\n",
    "from sklearn.tree import export_graphviz\n",
    "from pydot import graph_from_dot_data\n",
    "import pandas as pd\n",
    "import os\n",
    "\n",
    "np.random.seed(42)\n",
    "X, y = make_moons(n_samples=100, noise=0.25, random_state=53)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,y,random_state=0)\n",
    "tree_clf = DecisionTreeClassifier(max_depth=5)\n",
    "tree_clf.fit(X_train, y_train)\n",
    "\n",
    "export_graphviz(\n",
    "    tree_clf,\n",
    "    out_file=\"DataFiles/moons.dot\",\n",
    "    rounded=True,\n",
    "    filled=True\n",
    ")\n",
    "cmd = 'dot -Tpng DataFiles/moons.dot -o DataFiles/moons.png'\n",
    "os.system(cmd)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Other ways of visualizing the trees\n",
    "\n",
    "**Scikit-Learn** has also another way to visualize the trees which is very useful, here with the Iris data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 0.9166666666666666, 'x[2] <= 2.45\\ngini = 0.667\\nsamples = 150\\nvalue = [50, 50, 50]'),\n",
       " Text(0.4230769230769231, 0.75, 'gini = 0.0\\nsamples = 50\\nvalue = [50, 0, 0]'),\n",
       " Text(0.5769230769230769, 0.75, 'x[3] <= 1.75\\ngini = 0.5\\nsamples = 100\\nvalue = [0, 50, 50]'),\n",
       " Text(0.3076923076923077, 0.5833333333333334, 'x[2] <= 4.95\\ngini = 0.168\\nsamples = 54\\nvalue = [0, 49, 5]'),\n",
       " Text(0.15384615384615385, 0.4166666666666667, 'x[3] <= 1.65\\ngini = 0.041\\nsamples = 48\\nvalue = [0, 47, 1]'),\n",
       " Text(0.07692307692307693, 0.25, 'gini = 0.0\\nsamples = 47\\nvalue = [0, 47, 0]'),\n",
       " Text(0.23076923076923078, 0.25, 'gini = 0.0\\nsamples = 1\\nvalue = [0, 0, 1]'),\n",
       " Text(0.46153846153846156, 0.4166666666666667, 'x[3] <= 1.55\\ngini = 0.444\\nsamples = 6\\nvalue = [0, 2, 4]'),\n",
       " Text(0.38461538461538464, 0.25, 'gini = 0.0\\nsamples = 3\\nvalue = [0, 0, 3]'),\n",
       " Text(0.5384615384615384, 0.25, 'x[2] <= 5.45\\ngini = 0.444\\nsamples = 3\\nvalue = [0, 2, 1]'),\n",
       " Text(0.46153846153846156, 0.08333333333333333, 'gini = 0.0\\nsamples = 2\\nvalue = [0, 2, 0]'),\n",
       " Text(0.6153846153846154, 0.08333333333333333, 'gini = 0.0\\nsamples = 1\\nvalue = [0, 0, 1]'),\n",
       " Text(0.8461538461538461, 0.5833333333333334, 'x[2] <= 4.85\\ngini = 0.043\\nsamples = 46\\nvalue = [0, 1, 45]'),\n",
       " Text(0.7692307692307693, 0.4166666666666667, 'x[1] <= 3.1\\ngini = 0.444\\nsamples = 3\\nvalue = [0, 1, 2]'),\n",
       " Text(0.6923076923076923, 0.25, 'gini = 0.0\\nsamples = 2\\nvalue = [0, 0, 2]'),\n",
       " Text(0.8461538461538461, 0.25, 'gini = 0.0\\nsamples = 1\\nvalue = [0, 1, 0]'),\n",
       " Text(0.9230769230769231, 0.4166666666666667, 'gini = 0.0\\nsamples = 43\\nvalue = [0, 0, 43]')]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "filenames": {
       "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapter6_24_1.png"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.datasets import load_iris\n",
    "from sklearn import tree\n",
    "X, y = load_iris(return_X_y=True)\n",
    "tree_clf = tree.DecisionTreeClassifier()\n",
    "tree_clf = tree_clf.fit(X, y)\n",
    "# and then plot the tree\n",
    "tree.plot_tree(tree_clf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Alternatively, the tree can also be exported in textual format with the function exporttext.\n",
    "This method doesn’t require the installation of external libraries and is more compact:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "|--- petal width (cm) <= 0.80\n",
      "|   |--- class: 0\n",
      "|--- petal width (cm) >  0.80\n",
      "|   |--- petal width (cm) <= 1.75\n",
      "|   |   |--- class: 1\n",
      "|   |--- petal width (cm) >  1.75\n",
      "|   |   |--- class: 2\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.datasets import load_iris\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.tree import export_text\n",
    "iris = load_iris()\n",
    "decision_tree = DecisionTreeClassifier(random_state=0, max_depth=2)\n",
    "decision_tree = decision_tree.fit(iris.data, iris.target)\n",
    "r = export_text(decision_tree, feature_names=iris['feature_names'])\n",
    "print(r)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Algorithms for Setting up Decision Trees\n",
    "\n",
    "Two algorithms stand out in the set up of decision trees:\n",
    "1. The CART (Classification And Regression Tree) algorithm for both classification and regression\n",
    "\n",
    "2. The ID3 algorithm based on the computation of the information gain for classification\n",
    "\n",
    "We discuss both algorithms with applications here. The popular library\n",
    "**Scikit-Learn** uses the CART algorithm. For classification problems\n",
    "you can use either the **gini** index or the **entropy** to split a tree\n",
    "in two branches.\n",
    "\n",
    "### The CART algorithm for Classification\n",
    "\n",
    "For classification, the CART algorithm splits the data set in two subsets using a single feature $k$ and a threshold $t_k$.\n",
    "This could be for example a threshold set by a number below a certain circumference of a malign tumor.\n",
    "\n",
    "How do we find these two quantities?\n",
    "We search for the pair $(k,t_k)$ that produces the purest subset using for example the **gini** factor $G$.\n",
    "The cost function it tries to minimize is then"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$\n",
    "C(k,t_k) = \\frac{m_{\\mathrm{left}}}{m}G_{\\mathrm{left}}+ \\frac{m_{\\mathrm{right}}}{m}G_{\\mathrm{right}},\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "where $G_{\\mathrm{left/right}}$ measures the impurity of the left/right subset  and $m_{\\mathrm{left/right}}$\n",
    " is the number of instances in the left/right subset\n",
    "\n",
    "Once it has successfully split the training set in two, it splits the subsets using the same logic, then the subsubsets\n",
    "and so on, recursively. It stops recursing once it reaches the maximum depth (defined by the\n",
    "$max\\_depth$ hyperparameter), or if it cannot find a split that will reduce impurity. A few other\n",
    "hyperparameters control additional stopping conditions such as the $min\\_samples\\_split$,\n",
    "$min\\_samples\\_leaf$, $min\\_weight\\_fraction\\_leaf$, and $max\\_leaf\\_nodes$.\n",
    "\n",
    "\n",
    "### The CART algorithm for Regression\n",
    "\n",
    "The CART algorithm for regression works is similar to the one for classification except that instead of trying to split the\n",
    "training set in a way that minimizes say the **gini** or **entropy** impurity, it now tries to split the training set in a way that minimizes our well-known mean-squared error (MSE). The cost function is now"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$\n",
    "C(k,t_k) = \\frac{m_{\\mathrm{left}}}{m}\\mathrm{MSE}_{\\mathrm{left}}+ \\frac{m_{\\mathrm{right}}}{m}\\mathrm{MSE}_{\\mathrm{right}}.\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here the MSE for a specific node is defined as"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$\n",
    "\\mathrm{MSE}_{\\mathrm{node}}=\\frac{1}{m_\\mathrm{node}}\\sum_{i\\in \\mathrm{node}}(\\overline{y}_{\\mathrm{node}}-y_i)^2,\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "with"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$\n",
    "\\overline{y}_{\\mathrm{node}}=\\frac{1}{m_\\mathrm{node}}\\sum_{i\\in \\mathrm{node}}y_i,\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "the mean value of all observations in a specific node.\n",
    "\n",
    "Without any regularization, the regression task for decision trees, \n",
    "just like for classification tasks, is  prone to overfitting.\n",
    "\n",
    "\n",
    "\n",
    "### Computing the Gini index\n",
    "\n",
    "The example we will look at is a classical one in many Machine\n",
    "Learning applications. Based on various meteorological features, we\n",
    "have several so-called attributes which decide whether we at the end\n",
    "will do some outdoor activity like skiing, going for a bike ride etc\n",
    "etc.  The table here contains the feautures **outlook**, **temperature**,\n",
    "**humidity** and **wind**.  The target or output is whether we ride\n",
    "(True=1) or whether we do something else that day (False=0). The\n",
    "attributes for each feature are then sunny, overcast and rain for the\n",
    "outlook, hot, cold and mild for temperature, high and normal for\n",
    "humidity and weak and strong for wind.\n",
    "\n",
    "The table here summarizes the various attributes and\n",
    "<table border=\"1\">\n",
    "<thead>\n",
    "<tr><th align=\"center\">Day</th> <th align=\"center\">Outlook </th> <th align=\"center\">Temperature</th> <th align=\"center\">Humidity</th> <th align=\"center\"> Wind </th> <th align=\"center\">Ride</th> </tr>\n",
    "</thead>\n",
    "<tbody>\n",
    "<tr><td align=\"center\">   1      </td> <td align=\"center\">   Sunny       </td> <td align=\"center\">   Hot            </td> <td align=\"center\">   High        </td> <td align=\"center\">   Weak      </td> <td align=\"center\">   0       </td> </tr>\n",
    "<tr><td align=\"center\">   2      </td> <td align=\"center\">   Sunny       </td> <td align=\"center\">   Hot            </td> <td align=\"center\">   High        </td> <td align=\"center\">   Strong    </td> <td align=\"center\">   1       </td> </tr>\n",
    "<tr><td align=\"center\">   3      </td> <td align=\"center\">   Overcast    </td> <td align=\"center\">   Hot            </td> <td align=\"center\">   High        </td> <td align=\"center\">   Weak      </td> <td align=\"center\">   1       </td> </tr>\n",
    "<tr><td align=\"center\">   4      </td> <td align=\"center\">   Rain        </td> <td align=\"center\">   Mild           </td> <td align=\"center\">   High        </td> <td align=\"center\">   Weak      </td> <td align=\"center\">   1       </td> </tr>\n",
    "<tr><td align=\"center\">   5      </td> <td align=\"center\">   Rain        </td> <td align=\"center\">   Cool           </td> <td align=\"center\">   Normal      </td> <td align=\"center\">   Weak      </td> <td align=\"center\">   1       </td> </tr>\n",
    "<tr><td align=\"center\">   6      </td> <td align=\"center\">   Rain        </td> <td align=\"center\">   Cool           </td> <td align=\"center\">   Normal      </td> <td align=\"center\">   Strong    </td> <td align=\"center\">   0       </td> </tr>\n",
    "<tr><td align=\"center\">   7      </td> <td align=\"center\">   Overcast    </td> <td align=\"center\">   Cool           </td> <td align=\"center\">   Normal      </td> <td align=\"center\">   Strong    </td> <td align=\"center\">   1       </td> </tr>\n",
    "<tr><td align=\"center\">   8      </td> <td align=\"center\">   Sunny       </td> <td align=\"center\">   Mild           </td> <td align=\"center\">   High        </td> <td align=\"center\">   Weak      </td> <td align=\"center\">   0       </td> </tr>\n",
    "<tr><td align=\"center\">   9      </td> <td align=\"center\">   Sunny       </td> <td align=\"center\">   Cool           </td> <td align=\"center\">   Normal      </td> <td align=\"center\">   Weak      </td> <td align=\"center\">   1       </td> </tr>\n",
    "<tr><td align=\"center\">   10     </td> <td align=\"center\">   Rain        </td> <td align=\"center\">   Mild           </td> <td align=\"center\">   Normal      </td> <td align=\"center\">   Weak      </td> <td align=\"center\">   1       </td> </tr>\n",
    "<tr><td align=\"center\">   11     </td> <td align=\"center\">   Sunny       </td> <td align=\"center\">   Mild           </td> <td align=\"center\">   Normal      </td> <td align=\"center\">   Strong    </td> <td align=\"center\">   1       </td> </tr>\n",
    "<tr><td align=\"center\">   12     </td> <td align=\"center\">   Overcast    </td> <td align=\"center\">   Mild           </td> <td align=\"center\">   High        </td> <td align=\"center\">   Strong    </td> <td align=\"center\">   1       </td> </tr>\n",
    "<tr><td align=\"center\">   13     </td> <td align=\"center\">   Overcast    </td> <td align=\"center\">   Hot            </td> <td align=\"center\">   Normal      </td> <td align=\"center\">   Weak      </td> <td align=\"center\">   1       </td> </tr>\n",
    "<tr><td align=\"center\">   14     </td> <td align=\"center\">   Rain        </td> <td align=\"center\">   Mild           </td> <td align=\"center\">   High        </td> <td align=\"center\">   Strong    </td> <td align=\"center\">   0       </td> </tr>\n",
    "</tbody>\n",
    "</table>\n",
    "\n",
    "### Simple Python Code to read in Data and perform Classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  (0, 0)\t1.0\n",
      "  (0, 7)\t1.0\n",
      "  (0, 9)\t1.0\n",
      "  (0, 13)\t1.0\n",
      "  (1, 3)\t1.0\n",
      "  (1, 5)\t1.0\n",
      "  (1, 8)\t1.0\n",
      "  (1, 12)\t1.0\n",
      "  (2, 3)\t1.0\n",
      "  (2, 5)\t1.0\n",
      "  (2, 8)\t1.0\n",
      "  (2, 11)\t1.0\n",
      "  (3, 1)\t1.0\n",
      "  (3, 5)\t1.0\n",
      "  (3, 8)\t1.0\n",
      "  (3, 12)\t1.0\n",
      "  (4, 2)\t1.0\n",
      "  (4, 6)\t1.0\n",
      "  (4, 8)\t1.0\n",
      "  (4, 12)\t1.0\n",
      "  (5, 2)\t1.0\n",
      "  (5, 4)\t1.0\n",
      "  (5, 10)\t1.0\n",
      "  (5, 12)\t1.0\n",
      "  (6, 2)\t1.0\n",
      "  :\t:\n",
      "  (8, 12)\t1.0\n",
      "  (9, 3)\t1.0\n",
      "  (9, 4)\t1.0\n",
      "  (9, 10)\t1.0\n",
      "  (9, 12)\t1.0\n",
      "  (10, 2)\t1.0\n",
      "  (10, 6)\t1.0\n",
      "  (10, 10)\t1.0\n",
      "  (10, 12)\t1.0\n",
      "  (11, 3)\t1.0\n",
      "  (11, 6)\t1.0\n",
      "  (11, 10)\t1.0\n",
      "  (11, 11)\t1.0\n",
      "  (12, 1)\t1.0\n",
      "  (12, 6)\t1.0\n",
      "  (12, 8)\t1.0\n",
      "  (12, 11)\t1.0\n",
      "  (13, 1)\t1.0\n",
      "  (13, 5)\t1.0\n",
      "  (13, 10)\t1.0\n",
      "  (13, 12)\t1.0\n",
      "  (14, 2)\t1.0\n",
      "  (14, 6)\t1.0\n",
      "  (14, 8)\t1.0\n",
      "  (14, 11)\t1.0\n",
      "Train set accuracy with Decision Tree: 0.73\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Common imports\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.tree import export_graphviz\n",
    "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from IPython.display import Image \n",
    "from pydot import graph_from_dot_data\n",
    "import os\n",
    "\n",
    "# Where to save the figures and data files\n",
    "PROJECT_ROOT_DIR = \"Results\"\n",
    "FIGURE_ID = \"Results/FigureFiles\"\n",
    "DATA_ID = \"DataFiles/\"\n",
    "\n",
    "if not os.path.exists(PROJECT_ROOT_DIR):\n",
    "    os.mkdir(PROJECT_ROOT_DIR)\n",
    "\n",
    "if not os.path.exists(FIGURE_ID):\n",
    "    os.makedirs(FIGURE_ID)\n",
    "\n",
    "if not os.path.exists(DATA_ID):\n",
    "    os.makedirs(DATA_ID)\n",
    "\n",
    "def image_path(fig_id):\n",
    "    return os.path.join(FIGURE_ID, fig_id)\n",
    "\n",
    "def data_path(dat_id):\n",
    "    return os.path.join(DATA_ID, dat_id)\n",
    "\n",
    "def save_fig(fig_id):\n",
    "    plt.savefig(image_path(fig_id) + \".png\", format='png')\n",
    "\n",
    "infile = open(data_path(\"rideclass.csv\"),'r')\n",
    "\n",
    "# Read the experimental data with Pandas\n",
    "from IPython.display import display\n",
    "ridedata = pd.read_csv(infile,names = ('Outlook','Temperature','Humidity','Wind','Ride'))\n",
    "ridedata = pd.DataFrame(ridedata)\n",
    "\n",
    "# Features and targets\n",
    "X = ridedata.loc[:, ridedata.columns != 'Ride'].values\n",
    "y = ridedata.loc[:, ridedata.columns == 'Ride'].values\n",
    "\n",
    "# Create the encoder.\n",
    "encoder = OneHotEncoder(handle_unknown=\"ignore\")\n",
    "# Assume for simplicity all features are categorical.\n",
    "encoder.fit(X)    \n",
    "# Apply the encoder.\n",
    "X = encoder.transform(X)\n",
    "print(X)\n",
    "# Then do a Classification tree\n",
    "tree_clf = DecisionTreeClassifier(max_depth=2)\n",
    "tree_clf.fit(X, y)\n",
    "print(\"Train set accuracy with Decision Tree: {:.2f}\".format(tree_clf.score(X,y)))\n",
    "#transfer to a decision tree graph\n",
    "export_graphviz(\n",
    "    tree_clf,\n",
    "    out_file=\"DataFiles/ride.dot\",\n",
    "    rounded=True,\n",
    "    filled=True\n",
    ")\n",
    "cmd = 'dot -Tpng DataFiles/cancer.dot -o DataFiles/cancer.png'\n",
    "os.system(cmd)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above functions (gini, entropy and misclassification error) are\n",
    "important components of the so-called CART algorithm. We will discuss\n",
    "this algorithm below after we have discussed the information gain\n",
    "algorithm ID3.\n",
    "\n",
    "In the example here we have converted all our attributes into numerical values $0,1,2$ etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X1 < 0.000 Gini=0.408\n",
      "X1 < 0.000 Gini=0.408\n",
      "X1 < 1.000 Gini=0.394\n",
      "X1 < 2.000 Gini=0.394\n",
      "X1 < 2.000 Gini=0.394\n",
      "X1 < 2.000 Gini=0.394\n",
      "X1 < 1.000 Gini=0.394\n",
      "X1 < 0.000 Gini=0.408\n",
      "X1 < 0.000 Gini=0.408\n",
      "X1 < 2.000 Gini=0.394\n",
      "X1 < 0.000 Gini=0.408\n",
      "X1 < 1.000 Gini=0.394\n",
      "X1 < 1.000 Gini=0.394\n",
      "X1 < 2.000 Gini=0.394\n",
      "X2 < 0.000 Gini=0.408\n",
      "X2 < 0.000 Gini=0.408\n",
      "X2 < 0.000 Gini=0.408\n",
      "X2 < 1.000 Gini=0.407\n",
      "X2 < 2.000 Gini=0.407\n",
      "X2 < 2.000 Gini=0.407\n",
      "X2 < 2.000 Gini=0.407\n",
      "X2 < 1.000 Gini=0.407\n",
      "X2 < 2.000 Gini=0.407\n",
      "X2 < 1.000 Gini=0.407\n",
      "X2 < 1.000 Gini=0.407\n",
      "X2 < 1.000 Gini=0.407\n",
      "X2 < 0.000 Gini=0.408\n",
      "X2 < 1.000 Gini=0.407\n",
      "X3 < 0.000 Gini=0.408\n",
      "X3 < 0.000 Gini=0.408\n",
      "X3 < 0.000 Gini=0.408\n",
      "X3 < 0.000 Gini=0.408\n",
      "X3 < 1.000 Gini=0.367\n",
      "X3 < 1.000 Gini=0.367\n",
      "X3 < 1.000 Gini=0.367\n",
      "X3 < 0.000 Gini=0.408\n",
      "X3 < 1.000 Gini=0.367\n",
      "X3 < 1.000 Gini=0.367\n",
      "X3 < 1.000 Gini=0.367\n",
      "X3 < 0.000 Gini=0.408\n",
      "X3 < 1.000 Gini=0.367\n",
      "X3 < 0.000 Gini=0.408\n",
      "X4 < 0.000 Gini=0.408\n",
      "X4 < 1.000 Gini=0.405\n",
      "X4 < 0.000 Gini=0.408\n",
      "X4 < 0.000 Gini=0.408\n",
      "X4 < 0.000 Gini=0.408\n",
      "X4 < 1.000 Gini=0.405\n",
      "X4 < 1.000 Gini=0.405\n",
      "X4 < 0.000 Gini=0.408\n",
      "X4 < 0.000 Gini=0.408\n",
      "X4 < 0.000 Gini=0.408\n",
      "X4 < 1.000 Gini=0.405\n",
      "X4 < 1.000 Gini=0.405\n",
      "X4 < 0.000 Gini=0.408\n",
      "X4 < 1.000 Gini=0.405\n",
      "Split: [X3 < 1.000]\n"
     ]
    }
   ],
   "source": [
    "# Split a dataset based on an attribute and an attribute value\n",
    "def test_split(index, value, dataset):\n",
    "\tleft, right = list(), list()\n",
    "\tfor row in dataset:\n",
    "\t\tif row[index] < value:\n",
    "\t\t\tleft.append(row)\n",
    "\t\telse:\n",
    "\t\t\tright.append(row)\n",
    "\treturn left, right\n",
    " \n",
    "# Calculate the Gini index for a split dataset\n",
    "def gini_index(groups, classes):\n",
    "\t# count all samples at split point\n",
    "\tn_instances = float(sum([len(group) for group in groups]))\n",
    "\t# sum weighted Gini index for each group\n",
    "\tgini = 0.0\n",
    "\tfor group in groups:\n",
    "\t\tsize = float(len(group))\n",
    "\t\t# avoid divide by zero\n",
    "\t\tif size == 0:\n",
    "\t\t\tcontinue\n",
    "\t\tscore = 0.0\n",
    "\t\t# score the group based on the score for each class\n",
    "\t\tfor class_val in classes:\n",
    "\t\t\tp = [row[-1] for row in group].count(class_val) / size\n",
    "\t\t\tscore += p * p\n",
    "\t\t# weight the group score by its relative size\n",
    "\t\tgini += (1.0 - score) * (size / n_instances)\n",
    "\treturn gini\n",
    "\n",
    "# Select the best split point for a dataset\n",
    "def get_split(dataset):\n",
    "\tclass_values = list(set(row[-1] for row in dataset))\n",
    "\tb_index, b_value, b_score, b_groups = 999, 999, 999, None\n",
    "\tfor index in range(len(dataset[0])-1):\n",
    "\t\tfor row in dataset:\n",
    "\t\t\tgroups = test_split(index, row[index], dataset)\n",
    "\t\t\tgini = gini_index(groups, class_values)\n",
    "\t\t\tprint('X%d < %.3f Gini=%.3f' % ((index+1), row[index], gini))\n",
    "\t\t\tif gini < b_score:\n",
    "\t\t\t\tb_index, b_value, b_score, b_groups = index, row[index], gini, groups\n",
    "\treturn {'index':b_index, 'value':b_value, 'groups':b_groups}\n",
    " \n",
    "dataset = [[0,0,0,0,0],\n",
    "            [0,0,0,1,1],\n",
    "            [1,0,0,0,1],\n",
    "            [2,1,0,0,1],\n",
    "            [2,2,1,0,1],\n",
    "            [2,2,1,1,0],\n",
    "            [1,2,1,1,1],\n",
    "            [0,1,0,0,0],\n",
    "            [0,2,1,0,1],\n",
    "            [2,1,1,0,1],\n",
    "            [0,1,1,1,1],\n",
    "            [1,1,0,1,1],\n",
    "            [1,0,1,0,1],\n",
    "            [2,1,0,1,0]]\n",
    "\n",
    "split = get_split(dataset)\n",
    "print('Split: [X%d < %.3f]' % ((split['index']+1), split['value']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Entropy and the ID3 algorithm\n",
    "\n",
    "The ID3 algorithm  learns decision trees by constructing\n",
    "them in a top down way, beginning with the question **which attribute should be tested at the root of the tree**?\n",
    "\n",
    "1. Each instance attribute is evaluated using a statistical test to determine how well it alone classifies the training examples.\n",
    "\n",
    "2. The best attribute is selected and used as the test at the root node of the tree.\n",
    "\n",
    "3. A descendant of the root node is then created for each possible value of this attribute.\n",
    "\n",
    "4. Training examples are sorted to the appropriate descendant node.\n",
    "\n",
    "5. The entire process is then repeated using the training examples associated with each descendant node to select the best attribute to test at that point in the tree.\n",
    "\n",
    "6. This forms a greedy search for an acceptable decision tree, in which the algorithm never backtracks to reconsider earlier choices. \n",
    "\n",
    "The ID3 algorithm selects which attribute to test at each node in the\n",
    "tree.\n",
    "\n",
    "We would like to select the attribute that is most useful for classifying\n",
    "examples.\n",
    "\n",
    "What is a good quantitative measure of the worth of an attribute?\n",
    "\n",
    "Information gain measures how well a given attribute separates the\n",
    "training examples according to their target classification.\n",
    "\n",
    "The ID3 algorithm uses this information gain measure to select among the candidate\n",
    "attributes at each step while growing the tree.\n",
    "\n",
    "\n",
    "### Cancer Data again now with Decision Trees and other Methods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(426, 30)\n",
      "(143, 30)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test set accuracy with Logistic Regression: 0.94\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test set accuracy with SVM: 0.63\n",
      "Test set accuracy with Decision Trees: 0.90\n",
      "Test set accuracy Logistic Regression with scaled data: 0.96\n",
      "Test set accuracy SVM with scaled data: 0.96\n",
      "Test set accuracy with Decision Trees and scaled data: 0.89\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from sklearn.model_selection import  train_test_split \n",
    "from sklearn.datasets import load_breast_cancer\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "# Load the data\n",
    "cancer = load_breast_cancer()\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(cancer.data,cancer.target,random_state=0)\n",
    "print(X_train.shape)\n",
    "print(X_test.shape)\n",
    "# Logistic Regression\n",
    "logreg = LogisticRegression(solver='lbfgs')\n",
    "logreg.fit(X_train, y_train)\n",
    "print(\"Test set accuracy with Logistic Regression: {:.2f}\".format(logreg.score(X_test,y_test)))\n",
    "# Support vector machine\n",
    "svm = SVC(gamma='auto', C=100)\n",
    "svm.fit(X_train, y_train)\n",
    "print(\"Test set accuracy with SVM: {:.2f}\".format(svm.score(X_test,y_test)))\n",
    "# Decision Trees\n",
    "deep_tree_clf = DecisionTreeClassifier(max_depth=None)\n",
    "deep_tree_clf.fit(X_train, y_train)\n",
    "print(\"Test set accuracy with Decision Trees: {:.2f}\".format(deep_tree_clf.score(X_test,y_test)))\n",
    "#now scale the data\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler()\n",
    "scaler.fit(X_train)\n",
    "X_train_scaled = scaler.transform(X_train)\n",
    "X_test_scaled = scaler.transform(X_test)\n",
    "# Logistic Regression\n",
    "logreg.fit(X_train_scaled, y_train)\n",
    "print(\"Test set accuracy Logistic Regression with scaled data: {:.2f}\".format(logreg.score(X_test_scaled,y_test)))\n",
    "# Support Vector Machine\n",
    "svm.fit(X_train_scaled, y_train)\n",
    "print(\"Test set accuracy SVM with scaled data: {:.2f}\".format(logreg.score(X_test_scaled,y_test)))\n",
    "# Decision Trees\n",
    "deep_tree_clf.fit(X_train_scaled, y_train)\n",
    "print(\"Test set accuracy with Decision Trees and scaled data: {:.2f}\".format(deep_tree_clf.score(X_test_scaled,y_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Another example, the moons again"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1100x400 with 2 Axes>"
      ]
     },
     "metadata": {
      "filenames": {
       "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapter6_42_0.png"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from __future__ import division, print_function, unicode_literals\n",
    "\n",
    "# Common imports\n",
    "import numpy as np\n",
    "import os\n",
    "\n",
    "# to make this notebook's output stable across runs\n",
    "np.random.seed(42)\n",
    "\n",
    "# To plot pretty figures\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.colors import ListedColormap\n",
    "plt.rcParams['axes.labelsize'] = 14\n",
    "plt.rcParams['xtick.labelsize'] = 12\n",
    "plt.rcParams['ytick.labelsize'] = 12\n",
    "\n",
    "\n",
    "from sklearn.svm import SVC\n",
    "from sklearn import datasets\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.datasets import make_moons\n",
    "from sklearn.tree import export_graphviz\n",
    "\n",
    "Xm, ym = make_moons(n_samples=100, noise=0.25, random_state=53)\n",
    "\n",
    "deep_tree_clf1 = DecisionTreeClassifier(random_state=42)\n",
    "deep_tree_clf2 = DecisionTreeClassifier(min_samples_leaf=4, random_state=42)\n",
    "deep_tree_clf1.fit(Xm, ym)\n",
    "deep_tree_clf2.fit(Xm, ym)\n",
    "\n",
    "\n",
    "def plot_decision_boundary(clf, X, y, axes=[0, 7.5, 0, 3], iris=True, legend=False, plot_training=True):\n",
    "    x1s = np.linspace(axes[0], axes[1], 100)\n",
    "    x2s = np.linspace(axes[2], axes[3], 100)\n",
    "    x1, x2 = np.meshgrid(x1s, x2s)\n",
    "    X_new = np.c_[x1.ravel(), x2.ravel()]\n",
    "    y_pred = clf.predict(X_new).reshape(x1.shape)\n",
    "    custom_cmap = ListedColormap(['#fafab0','#9898ff','#a0faa0'])\n",
    "    plt.contourf(x1, x2, y_pred, alpha=0.3, cmap=custom_cmap)\n",
    "    if not iris:\n",
    "        custom_cmap2 = ListedColormap(['#7d7d58','#4c4c7f','#507d50'])\n",
    "        plt.contour(x1, x2, y_pred, cmap=custom_cmap2, alpha=0.8)\n",
    "    if plot_training:\n",
    "        plt.plot(X[:, 0][y==0], X[:, 1][y==0], \"yo\", label=\"Iris-Setosa\")\n",
    "        plt.plot(X[:, 0][y==1], X[:, 1][y==1], \"bs\", label=\"Iris-Versicolor\")\n",
    "        plt.plot(X[:, 0][y==2], X[:, 1][y==2], \"g^\", label=\"Iris-Virginica\")\n",
    "        plt.axis(axes)\n",
    "    if iris:\n",
    "        plt.xlabel(\"Petal length\", fontsize=14)\n",
    "        plt.ylabel(\"Petal width\", fontsize=14)\n",
    "    else:\n",
    "        plt.xlabel(r\"$x_1$\", fontsize=18)\n",
    "        plt.ylabel(r\"$x_2$\", fontsize=18, rotation=0)\n",
    "    if legend:\n",
    "        plt.legend(loc=\"lower right\", fontsize=14)\n",
    "plt.figure(figsize=(11, 4))\n",
    "plt.subplot(121)\n",
    "plot_decision_boundary(deep_tree_clf1, Xm, ym, axes=[-1.5, 2.5, -1, 1.5], iris=False)\n",
    "plt.title(\"No restrictions\", fontsize=16)\n",
    "plt.subplot(122)\n",
    "plot_decision_boundary(deep_tree_clf2, Xm, ym, axes=[-1.5, 2.5, -1, 1.5], iris=False)\n",
    "plt.title(\"min_samples_leaf = {}\".format(deep_tree_clf2.min_samples_leaf), fontsize=14)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1100x400 with 2 Axes>"
      ]
     },
     "metadata": {
      "filenames": {
       "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapter6_43_0.png"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "np.random.seed(6)\n",
    "Xs = np.random.rand(100, 2) - 0.5\n",
    "ys = (Xs[:, 0] > 0).astype(np.float32) * 2\n",
    "\n",
    "angle = np.pi/4\n",
    "rotation_matrix = np.array([[np.cos(angle), -np.sin(angle)], [np.sin(angle), np.cos(angle)]])\n",
    "Xsr = Xs.dot(rotation_matrix)\n",
    "\n",
    "tree_clf_s = DecisionTreeClassifier(random_state=42)\n",
    "tree_clf_s.fit(Xs, ys)\n",
    "tree_clf_sr = DecisionTreeClassifier(random_state=42)\n",
    "tree_clf_sr.fit(Xsr, ys)\n",
    "\n",
    "plt.figure(figsize=(11, 4))\n",
    "plt.subplot(121)\n",
    "plot_decision_boundary(tree_clf_s, Xs, ys, axes=[-0.7, 0.7, -0.7, 0.7], iris=False)\n",
    "plt.subplot(122)\n",
    "plot_decision_boundary(tree_clf_sr, Xsr, ys, axes=[-0.7, 0.7, -0.7, 0.7], iris=False)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# Quadratic training set + noise\n",
    "np.random.seed(42)\n",
    "m = 200\n",
    "X = np.random.rand(m, 1)\n",
    "y = 4 * (X - 0.5) ** 2\n",
    "y = y + np.random.randn(m, 1) / 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeRegressor(max_depth=2, random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeRegressor</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeRegressor(max_depth=2, random_state=42)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeRegressor(max_depth=2, random_state=42)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor\n",
    "\n",
    "tree_reg = DecisionTreeRegressor(max_depth=2, random_state=42)\n",
    "tree_reg.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1100x400 with 2 Axes>"
      ]
     },
     "metadata": {
      "filenames": {
       "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapter6_46_0.png"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor\n",
    "\n",
    "tree_reg1 = DecisionTreeRegressor(random_state=42, max_depth=2)\n",
    "tree_reg2 = DecisionTreeRegressor(random_state=42, max_depth=3)\n",
    "tree_reg1.fit(X, y)\n",
    "tree_reg2.fit(X, y)\n",
    "\n",
    "def plot_regression_predictions(tree_reg, X, y, axes=[0, 1, -0.2, 1], ylabel=\"$y$\"):\n",
    "    x1 = np.linspace(axes[0], axes[1], 500).reshape(-1, 1)\n",
    "    y_pred = tree_reg.predict(x1)\n",
    "    plt.axis(axes)\n",
    "    plt.xlabel(\"$x_1$\", fontsize=18)\n",
    "    if ylabel:\n",
    "        plt.ylabel(ylabel, fontsize=18, rotation=0)\n",
    "    plt.plot(X, y, \"b.\")\n",
    "    plt.plot(x1, y_pred, \"r.-\", linewidth=2, label=r\"$\\hat{y}$\")\n",
    "\n",
    "plt.figure(figsize=(11, 4))\n",
    "plt.subplot(121)\n",
    "plot_regression_predictions(tree_reg1, X, y)\n",
    "for split, style in ((0.1973, \"k-\"), (0.0917, \"k--\"), (0.7718, \"k--\")):\n",
    "    plt.plot([split, split], [-0.2, 1], style, linewidth=2)\n",
    "plt.text(0.21, 0.65, \"Depth=0\", fontsize=15)\n",
    "plt.text(0.01, 0.2, \"Depth=1\", fontsize=13)\n",
    "plt.text(0.65, 0.8, \"Depth=1\", fontsize=13)\n",
    "plt.legend(loc=\"upper center\", fontsize=18)\n",
    "plt.title(\"max_depth=2\", fontsize=14)\n",
    "\n",
    "plt.subplot(122)\n",
    "plot_regression_predictions(tree_reg2, X, y, ylabel=None)\n",
    "for split, style in ((0.1973, \"k-\"), (0.0917, \"k--\"), (0.7718, \"k--\")):\n",
    "    plt.plot([split, split], [-0.2, 1], style, linewidth=2)\n",
    "for split in (0.0458, 0.1298, 0.2873, 0.9040):\n",
    "    plt.plot([split, split], [-0.2, 1], \"k:\", linewidth=1)\n",
    "plt.text(0.3, 0.5, \"Depth=2\", fontsize=13)\n",
    "plt.title(\"max_depth=3\", fontsize=14)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1100x400 with 2 Axes>"
      ]
     },
     "metadata": {
      "filenames": {
       "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapter6_47_0.png"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tree_reg1 = DecisionTreeRegressor(random_state=42)\n",
    "tree_reg2 = DecisionTreeRegressor(random_state=42, min_samples_leaf=10)\n",
    "tree_reg1.fit(X, y)\n",
    "tree_reg2.fit(X, y)\n",
    "\n",
    "x1 = np.linspace(0, 1, 500).reshape(-1, 1)\n",
    "y_pred1 = tree_reg1.predict(x1)\n",
    "y_pred2 = tree_reg2.predict(x1)\n",
    "\n",
    "plt.figure(figsize=(11, 4))\n",
    "\n",
    "plt.subplot(121)\n",
    "plt.plot(X, y, \"b.\")\n",
    "plt.plot(x1, y_pred1, \"r.-\", linewidth=2, label=r\"$\\hat{y}$\")\n",
    "plt.axis([0, 1, -0.2, 1.1])\n",
    "plt.xlabel(\"$x_1$\", fontsize=18)\n",
    "plt.ylabel(\"$y$\", fontsize=18, rotation=0)\n",
    "plt.legend(loc=\"upper center\", fontsize=18)\n",
    "plt.title(\"No restrictions\", fontsize=14)\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.plot(X, y, \"b.\")\n",
    "plt.plot(x1, y_pred2, \"r.-\", linewidth=2, label=r\"$\\hat{y}$\")\n",
    "plt.axis([0, 1, -0.2, 1.1])\n",
    "plt.xlabel(\"$x_1$\", fontsize=18)\n",
    "plt.title(\"min_samples_leaf={}\".format(tree_reg2.min_samples_leaf), fontsize=14)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pros and cons of trees, pros\n",
    "\n",
    "* White box, easy to interpret model. Some people believe that decision trees more closely mirror human decision-making than do the regression and classification approaches discussed earlier (think of support vector machines)\n",
    "\n",
    "* Trees are very easy to explain to people. In fact, they are even easier to explain than linear regression!\n",
    "\n",
    "* No feature normalization needed\n",
    "\n",
    "* Tree models can handle both continuous and categorical data (Classification and Regression Trees)\n",
    "\n",
    "* Can model nonlinear relationships\n",
    "\n",
    "* Can model interactions between the different descriptive features\n",
    "\n",
    "* Trees can be displayed graphically, and are easily interpreted even by a non-expert (especially if they are small)\n",
    "\n",
    "### Disadvantages\n",
    "\n",
    "* Unfortunately, trees generally do not have the same level of predictive accuracy as some of the other regression and classification approaches\n",
    "\n",
    "* If continuous features are used the tree may become quite large and hence less interpretable\n",
    "\n",
    "* Decision trees are prone to overfit the training data and hence do not well generalize the data if no stopping criteria or improvements like pruning, boosting or bagging are implemented\n",
    "\n",
    "* Small changes in the data may lead to a completely different tree. This issue can be addressed by using ensemble methods like bagging, boosting or random forests\n",
    "\n",
    "* Unbalanced datasets where some target feature values occur much more frequently than others may lead to biased trees since the frequently occurring feature values are preferred over the less frequently occurring ones. \n",
    "\n",
    "* If the number of features is relatively large (high dimensional) and the number of instances is relatively low, the tree might overfit the data\n",
    "\n",
    "* Features with many levels may be preferred over features with less levels since for them it is *more easy* to split the dataset such that the sub datasets only contain pure target feature values. This issue can be addressed by preferring for instance the information gain ratio as splitting criteria over information gain\n",
    "\n",
    "However, by aggregating many decision trees, using methods like\n",
    "bagging, random forests, and boosting, the predictive performance of\n",
    "trees can be substantially improved."
   ]
  }
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