diff --git a/doc/HandWrittenNotes/NotesSeptember4.pdf b/doc/HandWrittenNotes/NotesSeptember4.pdf new file mode 100644 index 000000000..d842df51e Binary files /dev/null and b/doc/HandWrittenNotes/NotesSeptember4.pdf differ diff --git a/doc/pub/Regression/ipynb/Regression.ipynb b/doc/pub/Regression/ipynb/Regression.ipynb index 0912fa8b5..3b7248be4 100644 --- a/doc/pub/Regression/ipynb/Regression.ipynb +++ b/doc/pub/Regression/ipynb/Regression.ipynb @@ -5591,7 +5591,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -5599,80 +5599,45 @@ "output_type": "stream", "text": [ "Polynomial degree: 0\n", - "Error: 0.3214960170351912\n", - "Bias^2: 0.3123314713548606\n", - "Var: 0.009164545680330616\n", - "0.3214960170351912 >= 0.3123314713548606 + 0.009164545680330616 = 0.3214960170351912\n", + "Error: 0.2532788185411167\n", + "Bias^2: 0.2526964484949394\n", + "Var: 0.0005823700461773749\n", + "0.2532788185411167 >= 0.2526964484949394 + 0.0005823700461773749 = 0.2532788185411168\n", "Polynomial degree: 1\n", - "Error: 0.08426840630693411\n", - "Bias^2: 0.07968918676726029\n", - "Var: 0.004579219539673836\n", - "0.08426840630693411 >= 0.07968918676726029 + 0.004579219539673836 = 0.08426840630693413\n", + "Error: 0.08455386893059441\n", + "Bias^2: 0.08422251109115914\n", + "Var: 0.0003313578394352724\n", + "0.08455386893059441 >= 0.08422251109115914 + 0.0003313578394352724 = 0.08455386893059441\n", "Polynomial degree: 2\n", - "Error: 0.10398646080125035\n", - "Bias^2: 0.10077114273548984\n", - "Var: 0.003215318065760509\n", - "0.10398646080125035 >= 0.10077114273548984 + 0.003215318065760509 = 0.10398646080125035\n", + "Error: 0.059233304118678526\n", + "Bias^2: 0.0586320532355154\n", + "Var: 0.0006012508831631393\n", + "0.059233304118678526 >= 0.0586320532355154 + 0.0006012508831631393 = 0.05923330411867854\n", "Polynomial degree: 3\n", - "Error: 0.06547790180152357\n", - "Bias^2: 0.06208238634231953\n", - "Var: 0.0033955154592040944\n", - "0.06547790180152357 >= 0.06208238634231953 + 0.0033955154592040944 = 0.06547790180152363\n", + "Error: 0.029801715226919333\n", + "Bias^2: 0.029452192264365906\n", + "Var: 0.000349522962553423\n", + "0.029801715226919333 >= 0.029452192264365906 + 0.000349522962553423 = 0.02980171522691933\n", "Polynomial degree: 4\n", - "Error: 0.06844519414009438\n", - "Bias^2: 0.06453579006728315\n", - "Var: 0.003909404072811231\n", - "0.06844519414009438 >= 0.06453579006728315 + 0.003909404072811231 = 0.06844519414009438\n", + "Error: 0.029794287278949182\n", + "Bias^2: 0.029348037991086092\n", + "Var: 0.00044624928786308614\n", + "0.029794287278949182 >= 0.029348037991086092 + 0.00044624928786308614 = 0.02979428727894918\n", "Polynomial degree: 5\n", - "Error: 0.05227921801205692\n", - "Bias^2: 0.04818727730430296\n", - "Var: 0.0040919407077539514\n", - "0.05227921801205692 >= 0.04818727730430296 + 0.0040919407077539514 = 0.05227921801205691\n", + "Error: 0.024440674046123795\n", + "Bias^2: 0.023999783622663227\n", + "Var: 0.0004408904234605626\n", + "0.024440674046123795 >= 0.023999783622663227 + 0.0004408904234605626 = 0.024440674046123788\n", "Polynomial degree: 6\n", - "Error: 0.03781367141738885\n", - "Bias^2: 0.033657685071527485\n", - "Var: 0.004155986345861374\n", - "0.03781367141738885 >= 0.033657685071527485 + 0.004155986345861374 = 0.03781367141738886\n", - "Polynomial degree: 7\n", - "Error: 0.027609773491022314\n", - "Bias^2: 0.02299949826036602\n", - "Var: 0.004610275230656294\n", - "0.027609773491022314 >= 0.02299949826036602 + 0.004610275230656294 = 0.027609773491022314\n", - "Polynomial degree: 8\n", - "Error: 0.017355848195591845\n", - "Bias^2: 0.01033172130665515\n", - "Var: 0.007024126888936694\n", - "0.017355848195591845 >= 0.01033172130665515 + 0.007024126888936694 = 0.01735584819559184\n", - "Polynomial degree: 9\n", - "Error: 0.026605727637176654\n", - "Bias^2: 0.010018312644139347\n", - "Var: 0.016587414993037307\n", - "0.026605727637176654 >= 0.010018312644139347 + 0.016587414993037307 = 0.026605727637176654\n", - "Polynomial degree: 10\n", - "Error: 0.02159270458799264\n", - "Bias^2: 0.010516485576652856\n", - "Var: 0.011076219011339788\n", - "0.02159270458799264 >= 0.010516485576652856 + 0.011076219011339788 = 0.021592704587992645\n", - "Polynomial degree: 11\n", - "Error: 0.07160048164248561\n", - "Bias^2: 0.014436800088969727\n", - "Var: 0.05716368155351588\n", - "0.07160048164248561 >= 0.014436800088969727 + 0.05716368155351588 = 0.07160048164248561\n", - "Polynomial degree: 12\n", - "Error: 0.11547777218940905\n", - "Bias^2: 0.016285782696075054\n", - "Var: 0.099191989493334\n", - "0.11547777218940905 >= 0.016285782696075054 + 0.099191989493334 = 0.11547777218940906\n", - "Polynomial degree: 13\n", - "Error: 0.22842468702288576\n", - "Bias^2: 0.01975416527179247\n", - "Var: 0.20867052175109335\n", - "0.22842468702288576 >= 0.01975416527179247 + 0.20867052175109335 = 0.22842468702288582\n" + "Error: 0.02059388133830918\n", + "Bias^2: 0.02004411755832111\n", + "Var: 0.0005497637799880787\n", + "0.02059388133830918 >= 0.02004411755832111 + 0.0005497637799880787 = 0.020593881338309188\n" ] }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -5694,9 +5659,9 @@ "\n", "np.random.seed(2018)\n", "\n", - "n = 40\n", + "n = 500\n", "n_boostraps = 100\n", - "maxdegree = 14\n", + "maxdegree =7\n", "\n", "\n", "# Make data set.\n", @@ -5708,6 +5673,7 @@ "polydegree = np.zeros(maxdegree)\n", "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2)\n", "\n", + "# complexity\n", "for degree in range(maxdegree):\n", " model = make_pipeline(PolynomialFeatures(degree=degree), LinearRegression(fit_intercept=False))\n", " y_pred = np.empty((y_test.shape[0], n_boostraps))\n", @@ -6041,9 +6007,22 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 5, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n",