update week44

This commit is contained in:
mhjensen
2020-10-30 11:55:57 +01:00
parent 53bf4090f2
commit 0a3c8c71aa
53 changed files with 175 additions and 175 deletions
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@@ -143,7 +143,7 @@ MathJax.Hub.Config({
<span class="icon-bar"></span>
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<a class="navbar-brand" href="week44-bs.html">week 44: From Decision Trees to Bagging methods</a>
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@@ -215,7 +215,7 @@ MathJax.Hub.Config({
<div class="jumbotron">
<center><h1>week 44: From Decision Trees to Bagging methods</h1></center> <!-- document title -->
<center><h1>Week 44: From Decision Trees to Bagging methods</h1></center> <!-- document title -->
<p>
<!-- author(s): Morten Hjorth-Jensen -->
+7 -7
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@@ -3,9 +3,9 @@
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<meta name="description" content="week 44: From Decision Trees to Bagging methods">
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<title>week 44: From Decision Trees to Bagging methods</title>
<title>Week 44: From Decision Trees to Bagging methods</title>
@@ -132,7 +132,7 @@ MathJax.Hub.Config({
<center><h1 style="text-align: center;">week 44: From Decision Trees to Bagging methods</h1></center> <!-- document title -->
<center><h1 style="text-align: center;">Week 44: From Decision Trees to Bagging methods</h1></center> <!-- document title -->
<p>
<!-- author(s): Morten Hjorth-Jensen -->
@@ -1732,11 +1732,11 @@ simpleprediction = simpletree.predict(X_test_scaled)
<span style="color: #658b00">print</span>(<span style="color: #CD5555">&#39;Bias^2:&#39;</span>, bias[degree])
<span style="color: #658b00">print</span>(<span style="color: #CD5555">&#39;Var:&#39;</span>, variance[degree])
<span style="color: #658b00">print</span>(<span style="color: #CD5555">&#39;{} &gt;= {} + {} = {}&#39;</span>.format(error[degree], bias[degree], variance[degree], bias[degree]+variance[degree]))
mse_simpletree = np.mean( np.mean((y_test - simpleprediction)**<span style="color: #B452CD">2</span>)
mse_simpletree= np.mean( np.mean((y_test - simpleprediction)**<span style="color: #B452CD">2</span>)
<span style="color: #658b00">print</span>(mse_simpletree)
plt.xlim(<span style="color: #B452CD">1</span>,maxdepth)
plt.plot(polydegree, error, label=<span style="color: #CD5555">&#39;MSE simple tree&#39;</span>)
plt.plot(polydegree, mse_simpletree, label=<span style="color: #CD5555">&#39;MSE for Bootstrap&#39;</span>)
plt.plot(polydegree, error, label=<span style="color: #CD5555">&#39;MSE&#39;</span>)
plt.plot(polydegree, bias, label=<span style="color: #CD5555">&#39;bias&#39;</span>)
plt.plot(polydegree, variance, label=<span style="color: #CD5555">&#39;Variance&#39;</span>)
plt.legend()
+7 -7
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@@ -7,9 +7,9 @@ Automatically generated HTML file from DocOnce source
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@@ -159,7 +159,7 @@ MathJax.Hub.Config({
<center><h1>week 44: From Decision Trees to Bagging methods</h1></center> <!-- document title -->
<center><h1>Week 44: From Decision Trees to Bagging methods</h1></center> <!-- document title -->
<p>
<!-- author(s): Morten Hjorth-Jensen -->
@@ -1704,11 +1704,11 @@ simpleprediction = simpletree.predict(X_test_scaled)
<span style="color: #658b00">print</span>(<span style="color: #CD5555">&#39;Bias^2:&#39;</span>, bias[degree])
<span style="color: #658b00">print</span>(<span style="color: #CD5555">&#39;Var:&#39;</span>, variance[degree])
<span style="color: #658b00">print</span>(<span style="color: #CD5555">&#39;{} &gt;= {} + {} = {}&#39;</span>.format(error[degree], bias[degree], variance[degree], bias[degree]+variance[degree]))
mse_simpletree = np.mean( np.mean((y_test - simpleprediction)**<span style="color: #B452CD">2</span>)
mse_simpletree= np.mean( np.mean((y_test - simpleprediction)**<span style="color: #B452CD">2</span>)
<span style="color: #658b00">print</span>(mse_simpletree)
plt.xlim(<span style="color: #B452CD">1</span>,maxdepth)
plt.plot(polydegree, error, label=<span style="color: #CD5555">&#39;MSE simple tree&#39;</span>)
plt.plot(polydegree, mse_simpletree, label=<span style="color: #CD5555">&#39;MSE for Bootstrap&#39;</span>)
plt.plot(polydegree, error, label=<span style="color: #CD5555">&#39;MSE&#39;</span>)
plt.plot(polydegree, bias, label=<span style="color: #CD5555">&#39;bias&#39;</span>)
plt.plot(polydegree, variance, label=<span style="color: #CD5555">&#39;Variance&#39;</span>)
plt.legend()
+7 -7
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@@ -7,9 +7,9 @@ Automatically generated HTML file from DocOnce source
<meta http-equiv="Content-Type" content="text/html; charset=utf-8" />
<meta name="generator" content="DocOnce: https://github.com/hplgit/doconce/" />
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<meta name="description" content="week 44: From Decision Trees to Bagging methods">
<meta name="description" content="Week 44: From Decision Trees to Bagging methods">
<title>week 44: From Decision Trees to Bagging methods</title>
<title>Week 44: From Decision Trees to Bagging methods</title>
<style type="text/css">
@@ -164,7 +164,7 @@ MathJax.Hub.Config({
<center><h1>week 44: From Decision Trees to Bagging methods</h1></center> <!-- document title -->
<center><h1>Week 44: From Decision Trees to Bagging methods</h1></center> <!-- document title -->
<p>
<!-- author(s): Morten Hjorth-Jensen -->
@@ -1709,11 +1709,11 @@ simpleprediction <span style="color: #666666">=</span> simpletree<span style="co
<span style="color: #008000">print</span>(<span style="color: #BA2121">&#39;Bias^2:&#39;</span>, bias[degree])
<span style="color: #008000">print</span>(<span style="color: #BA2121">&#39;Var:&#39;</span>, variance[degree])
<span style="color: #008000">print</span>(<span style="color: #BA2121">&#39;</span><span style="color: #BB6688; font-weight: bold">{}</span><span style="color: #BA2121"> &gt;= </span><span style="color: #BB6688; font-weight: bold">{}</span><span style="color: #BA2121"> + </span><span style="color: #BB6688; font-weight: bold">{}</span><span style="color: #BA2121"> = </span><span style="color: #BB6688; font-weight: bold">{}</span><span style="color: #BA2121">&#39;</span><span style="color: #666666">.</span>format(error[degree], bias[degree], variance[degree], bias[degree]<span style="color: #666666">+</span>variance[degree]))
mse_simpletree <span style="color: #666666">=</span> np<span style="color: #666666">.</span>mean( np<span style="color: #666666">.</span>mean((y_test <span style="color: #666666">-</span> simpleprediction)<span style="color: #666666">**2</span>)
mse_simpletree<span style="color: #666666">=</span> np<span style="color: #666666">.</span>mean( np<span style="color: #666666">.</span>mean((y_test <span style="color: #666666">-</span> simpleprediction)<span style="color: #666666">**2</span>)
<span style="color: #008000">print</span>(mse_simpletree)
plt<span style="color: #666666">.</span>xlim(<span style="color: #666666">1</span>,maxdepth)
plt<span style="color: #666666">.</span>plot(polydegree, error, label<span style="color: #666666">=</span><span style="color: #BA2121">&#39;MSE simple tree&#39;</span>)
plt<span style="color: #666666">.</span>plot(polydegree, mse_simpletree, label<span style="color: #666666">=</span><span style="color: #BA2121">&#39;MSE for Bootstrap&#39;</span>)
plt<span style="color: #666666">.</span>plot(polydegree, error, label<span style="color: #666666">=</span><span style="color: #BA2121">&#39;MSE&#39;</span>)
plt<span style="color: #666666">.</span>plot(polydegree, bias, label<span style="color: #666666">=</span><span style="color: #BA2121">&#39;bias&#39;</span>)
plt<span style="color: #666666">.</span>plot(polydegree, variance, label<span style="color: #666666">=</span><span style="color: #BA2121">&#39;Variance&#39;</span>)
plt<span style="color: #666666">.</span>legend()
+8 -8
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@@ -10,7 +10,7 @@ edge [fontname=helvetica] ;
2 -> 3 ;
4 [label="gini = 0.0\nsamples = 239\nvalue = [[239, 0]\n[0, 239]]", fillcolor="#e58139"] ;
3 -> 4 ;
5 [label="concavity error <= 0.017\ngini = 0.444\nsamples = 3\nvalue = [[2, 1]\n[1, 2]]", fillcolor="#fdf6f0"] ;
5 [label="mean area <= 469.25\ngini = 0.444\nsamples = 3\nvalue = [[2, 1]\n[1, 2]]", fillcolor="#fdf6f0"] ;
3 -> 5 ;
6 [label="gini = 0.0\nsamples = 1\nvalue = [[0, 1]\n[1, 0]]", fillcolor="#e58139"] ;
5 -> 6 ;
@@ -22,7 +22,7 @@ edge [fontname=helvetica] ;
8 -> 9 ;
10 [label="gini = 0.0\nsamples = 3\nvalue = [[0, 3]\n[3, 0]]", fillcolor="#e58139"] ;
8 -> 10 ;
11 [label="worst texture <= 24.785\ngini = 0.278\nsamples = 6\nvalue = [[1, 5]\n[5, 1]]", fillcolor="#f4caac"] ;
11 [label="area error <= 13.475\ngini = 0.278\nsamples = 6\nvalue = [[1, 5]\n[5, 1]]", fillcolor="#f4caac"] ;
1 -> 11 ;
12 [label="gini = 0.0\nsamples = 1\nvalue = [[1, 0]\n[0, 1]]", fillcolor="#e58139"] ;
11 -> 12 ;
@@ -30,11 +30,11 @@ edge [fontname=helvetica] ;
11 -> 13 ;
14 [label="worst texture <= 20.645\ngini = 0.202\nsamples = 167\nvalue = [[19, 148]\n[148, 19]]", fillcolor="#f0b68c"] ;
0 -> 14 [labeldistance=2.5, labelangle=-45, headlabel="False"] ;
15 [label="worst area <= 964.4\ngini = 0.375\nsamples = 16\nvalue = [[12, 4]\n[4, 12]]", fillcolor="#f9e3d4"] ;
15 [label="worst concavity <= 0.318\ngini = 0.375\nsamples = 16\nvalue = [[12, 4]\n[4, 12]]", fillcolor="#f9e3d4"] ;
14 -> 15 ;
16 [label="gini = 0.0\nsamples = 11\nvalue = [[11, 0]\n[0, 11]]", fillcolor="#e58139"] ;
15 -> 16 ;
17 [label="mean concavity <= 0.06\ngini = 0.32\nsamples = 5\nvalue = [[1, 4]\n[4, 1]]", fillcolor="#f6d5bd"] ;
17 [label="worst area <= 894.55\ngini = 0.32\nsamples = 5\nvalue = [[1, 4]\n[4, 1]]", fillcolor="#f6d5bd"] ;
15 -> 17 ;
18 [label="gini = 0.0\nsamples = 1\nvalue = [[1, 0]\n[0, 1]]", fillcolor="#e58139"] ;
17 -> 18 ;
@@ -42,16 +42,16 @@ edge [fontname=helvetica] ;
17 -> 19 ;
20 [label="mean concave points <= 0.049\ngini = 0.088\nsamples = 151\nvalue = [[7, 144]\n[144, 7]]", fillcolor="#ea985d"] ;
14 -> 20 ;
21 [label="concave points error <= 0.01\ngini = 0.48\nsamples = 15\nvalue = [[6, 9]\n[9, 6]]", fillcolor="#ffffff"] ;
21 [label="compactness error <= 0.016\ngini = 0.48\nsamples = 15\nvalue = [[6, 9]\n[9, 6]]", fillcolor="#ffffff"] ;
20 -> 21 ;
22 [label="gini = 0.0\nsamples = 9\nvalue = [[0, 9]\n[9, 0]]", fillcolor="#e58139"] ;
21 -> 22 ;
23 [label="gini = 0.0\nsamples = 6\nvalue = [[6, 0]\n[0, 6]]", fillcolor="#e58139"] ;
21 -> 23 ;
24 [label="mean smoothness <= 0.079\ngini = 0.015\nsamples = 136\nvalue = [[1, 135]\n[135, 1]]", fillcolor="#e6853f"] ;
24 [label="fractal dimension error <= 0.013\ngini = 0.015\nsamples = 136\nvalue = [[1, 135]\n[135, 1]]", fillcolor="#e6853f"] ;
20 -> 24 ;
25 [label="gini = 0.0\nsamples = 1\nvalue = [[1, 0]\n[0, 1]]", fillcolor="#e58139"] ;
25 [label="gini = 0.0\nsamples = 135\nvalue = [[0, 135]\n[135, 0]]", fillcolor="#e58139"] ;
24 -> 25 ;
26 [label="gini = 0.0\nsamples = 135\nvalue = [[0, 135]\n[135, 0]]", fillcolor="#e58139"] ;
26 [label="gini = 0.0\nsamples = 1\nvalue = [[1, 0]\n[0, 1]]", fillcolor="#e58139"] ;
24 -> 26 ;
}
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+1 -1
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@@ -1,7 +1,7 @@
digraph Tree {
node [shape=box, style="filled, rounded", color="black", fontname=helvetica] ;
edge [fontname=helvetica] ;
0 [label="X[7] <= 0.5\ngini = 0.48\nsamples = 15\nvalue = [4, 10, 1]", fillcolor="#93f1ba"] ;
0 [label="X[9] <= 0.5\ngini = 0.48\nsamples = 15\nvalue = [4, 10, 1]", fillcolor="#93f1ba"] ;
1 [label="X[1] <= 0.5\ngini = 0.408\nsamples = 14\nvalue = [4, 10, 0]", fillcolor="#88efb3"] ;
0 -> 1 [labeldistance=2.5, labelangle=45, headlabel="True"] ;
2 [label="gini = 0.48\nsamples = 10\nvalue = [4, 6, 0]", fillcolor="#bdf6d5"] ;
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+6 -6
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@@ -4,8 +4,8 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"<!-- dom:TITLE: week 44: From Decision Trees to Bagging methods -->\n",
"# week 44: From Decision Trees to Bagging methods\n",
"<!-- dom:TITLE: Week 44: From Decision Trees to Bagging methods -->\n",
"# Week 44: From Decision Trees to Bagging methods\n",
"<!-- dom:AUTHOR: Morten Hjorth-Jensen at Department of Physics, University of Oslo & Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University -->\n",
"<!-- Author: --> \n",
"**Morten Hjorth-Jensen**, Department of Physics, University of Oslo and Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University\n",
@@ -1775,11 +1775,11 @@
" print('Bias^2:', bias[degree])\n",
" print('Var:', variance[degree])\n",
" print('{} >= {} + {} = {}'.format(error[degree], bias[degree], variance[degree], bias[degree]+variance[degree]))\n",
"\n",
"mse_simpletree = np.mean( np.mean((y_test - simpleprediction)**2)\n",
" \n",
"mse_simpletree= np.mean( np.mean((y_test - simpleprediction)**2)\n",
"print(mse_simpletree)\n",
"plt.xlim(1,maxdepth)\n",
"plt.plot(polydegree, error, label='MSE simple tree')\n",
"plt.plot(polydegree, mse_simpletree, label='MSE for Bootstrap')\n",
"plt.plot(polydegree, error, label='MSE')\n",
"plt.plot(polydegree, bias, label='bias')\n",
"plt.plot(polydegree, variance, label='Variance')\n",
"plt.legend()\n",
+5 -5
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@@ -1,4 +1,4 @@
TITLE: week 44: From Decision Trees to Bagging methods
TITLE: Week 44: From Decision Trees to Bagging methods
AUTHOR: Morten Hjorth-Jensen {copyright, 1999-present|CC BY-NC} at Department of Physics, University of Oslo & Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University
DATE: today
@@ -1382,11 +1382,11 @@ for degree in range(1,maxdepth):
print('Bias^2:', bias[degree])
print('Var:', variance[degree])
print('{} >= {} + {} = {}'.format(error[degree], bias[degree], variance[degree], bias[degree]+variance[degree]))
mse_simpletree = np.mean( np.mean((y_test - simpleprediction)**2)
mse_simpletree= np.mean( np.mean((y_test - simpleprediction)**2)
print(mse_simpletree)
plt.xlim(1,maxdepth)
plt.plot(polydegree, error, label='MSE simple tree')
plt.plot(polydegree, mse_simpletree, label='MSE for Bootstrap')
plt.plot(polydegree, error, label='MSE')
plt.plot(polydegree, bias, label='bias')
plt.plot(polydegree, variance, label='Variance')
plt.legend()