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Mustafa Aldemir
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updated installation steps, added new examples
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‎2_MachineLearning.pdf

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‎CNN.ipynb

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‎CompareRegressionMethods.ipynb

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"cell_type": "markdown",
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"source": [
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"Example of Regression Analysis Using the Boston Housing Data Set\n",
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"http://facweb.cs.depaul.edu/mobasher/classes/CSC478/Data/housing-dscr.txt"
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"Example of Regression Analysis Using the Boston Housing Data Set: http://facweb.cs.depaul.edu/mobasher/classes/CSC478/Data/housing-dscr.txt\n",
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"\n",
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"Source: http://facweb.cs.depaul.edu/mobasher/classes/CSC478/Notes/IPython%20Notebook%20-%20Regression.html"
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{
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"pl.show()"
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Comparisons regression methods\n",
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"#### let's parametrize the regression methods\n"
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]
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},
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"execution_count": 44,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Method: linear regression\n",
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"RMSE on training: 4.6795\n",
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"RMSE on 10-fold CV: 5.8819\n",
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"\n",
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"\n",
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"Method: lasso\n",
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"RMSE on training: 4.8570\n",
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"RMSE on 10-fold CV: 5.7675\n",
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"\n",
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"\n",
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"Method: ridge\n",
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"RMSE on training: 4.6822\n",
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"RMSE on 10-fold CV: 5.8535\n",
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"\n",
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"\n",
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"Method: elastic-net\n",
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"RMSE on training: 4.9072\n",
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"RMSE on 10-fold CV: 5.4936\n",
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"\n",
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"\n"
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]
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}
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],
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"source": [
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"a = 0.3\n",
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"for name,met in [\n",
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" ('linear regression', LinearRegression()),\n",
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" ('lasso', Lasso(fit_intercept=True, alpha=a)),\n",
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" ('ridge', Ridge(fit_intercept=True, alpha=a)),\n",
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" ('elastic-net', ElasticNet(fit_intercept=True, alpha=a))\n",
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" ]:\n",
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" met.fit(x,y)\n",
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" # p = np.array([met.predict(xi) for xi in x])\n",
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" p = met.predict(x)\n",
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" e = p-y\n",
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" total_error = np.dot(e,e)\n",
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" rmse_train = np.sqrt(total_error/len(p))\n",
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"\n",
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" kf = KFold(len(x), n_folds=10)\n",
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" err = 0\n",
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" for train,test in kf:\n",
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" met.fit(x[train],y[train])\n",
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" p = met.predict(x[test])\n",
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" e = p-y[test]\n",
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" err += np.dot(e,e)\n",
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"\n",
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" rmse_10cv = np.sqrt(err/len(x))\n",
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" print('Method: %s' %name)\n",
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" print('RMSE on training: %.4f' %rmse_train)\n",
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" print('RMSE on 10-fold CV: %.4f' %rmse_10cv)\n",
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" print('\\n')"
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]
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},
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"cell_type": "code",
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"execution_count": null,
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.6.0"
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"version": "3.5.3"
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}
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},
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"nbformat": 4,

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