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pull/6/merge
cif 2 years ago
parent 8784fdc773
commit f43cde73e4
2 changed files with 98 additions and 24 deletions
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112
ml1/2_5_2_Decision_Tree_Model.ipynb
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@ -74,7 +74,7 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
@ -124,9 +124,20 @@
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"DecisionTreeClassifier(max_depth=3, random_state=1)"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn.tree import DecisionTreeClassifier\n",
"import numpy as np\n",
@ -145,9 +156,24 @@
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Prediction [1 0 1 1 1 0 0 1 0 2 0 0 1 2 0 1 2 2 1 1 0 0 2 0 0 2 1 1 2 2 2 2 0 0 1 1 0\n",
" 1 2 1 2 0 2 0 1 0 2 1 0 2 2 0 0 2 0 0 0 2 2 0 1 0 1 0 1 1 1 1 1 0 1 0 1 2\n",
" 0 0 0 0 2 2 0 1 1 2 1 0 0 2 1 1 0 1 1 0 2 1 2 1 2 0 1 0 0 0 2 1 2 1 2 1 2\n",
" 0]\n",
"Expected [1 0 1 1 1 0 0 1 0 2 0 0 1 2 0 1 2 2 1 1 0 0 2 0 0 2 1 1 2 2 2 2 0 0 1 1 0\n",
" 1 2 1 2 0 2 0 1 0 2 1 0 2 2 0 0 2 0 0 0 2 2 0 1 0 1 0 1 1 1 1 1 0 1 0 1 2\n",
" 0 0 0 0 2 2 0 1 1 2 1 0 0 1 1 1 0 1 1 0 2 2 2 1 2 0 1 0 0 0 2 1 2 1 2 1 2\n",
" 0]\n"
]
}
],
"source": [
"print(\"Prediction \", model.predict(x_train))\n",
"print(\"Expected \", y_train)"
@ -162,9 +188,26 @@
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Predicted probabilities [[0. 0.97368421 0.02631579]\n",
" [1. 0. 0. ]\n",
" [0. 0.97368421 0.02631579]\n",
" [0. 0.97368421 0.02631579]\n",
" [0. 0.97368421 0.02631579]\n",
" [1. 0. 0. ]\n",
" [1. 0. 0. ]\n",
" [0. 0.97368421 0.02631579]\n",
" [1. 0. 0. ]\n",
" [0. 0. 1. ]]\n"
]
}
],
"source": [
"# Print the \n",
"print(\"Predicted probabilities\", model.predict_proba(x_train[:10]))"
@ -172,9 +215,17 @@
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Accuracy in training 0.9821428571428571\n"
]
}
],
"source": [
"# Evaluate Accuracy in training\n",
"\n",
@ -185,9 +236,17 @@
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Accuracy in testing 0.9210526315789473\n"
]
}
],
"source": [
"# Now we evaluate error in testing\n",
"y_test_pred = model.predict(x_test)\n",
@ -209,9 +268,24 @@
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"execution_count": 7,
"metadata": {},
"outputs": [
{
"ename": "InvocationException",
"evalue": "GraphViz's executables not found",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mInvocationException\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m/tmp/ipykernel_47326/3723147494.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 12\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 13\u001b[0m \u001b[0mgraph\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpydot\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgraph_from_dot_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdot_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetvalue\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m \u001b[0mgraph\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwrite_png\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'iris-tree.png'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 15\u001b[0m \u001b[0mImage\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgraph\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcreate_png\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/pydotplus/graphviz.py\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(path, f, prog)\u001b[0m\n\u001b[1;32m 1808\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mpath\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1809\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfrmt\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1810\u001b[0;31m \u001b[0mprog\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprog\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwrite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mformat\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprog\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mprog\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1811\u001b[0m )\n\u001b[1;32m 1812\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/pydotplus/graphviz.py\u001b[0m in \u001b[0;36mwrite\u001b[0;34m(self, path, prog, format)\u001b[0m\n\u001b[1;32m 1916\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1917\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1918\u001b[0;31m \u001b[0mfobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwrite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcreate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprog\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mformat\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1919\u001b[0m \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1920\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mclose\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/pydotplus/graphviz.py\u001b[0m in \u001b[0;36mcreate\u001b[0;34m(self, prog, format)\u001b[0m\n\u001b[1;32m 1957\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprogs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfind_graphviz\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1958\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprogs\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1959\u001b[0;31m raise InvocationException(\n\u001b[0m\u001b[1;32m 1960\u001b[0m 'GraphViz\\'s executables not found')\n\u001b[1;32m 1961\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mInvocationException\u001b[0m: GraphViz's executables not found"
]
}
],
"source": [
"from IPython.display import Image \n",
"from six import StringIO\n",

10
ml1/2_6_Model_Tuning.ipynb
Unescape Escape View File

@ -517,9 +517,9 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"* [Plot the decision surface of a decision tree on the iris dataset](http://scikit-learn.org/stable/auto_examples/tree/plot_iris.html)\n",
"* [Learning scikit-learn: Machine Learning in Python](http://proquest.safaribooksonline.com/book/programming/python/9781783281930/1dot-machine-learning-a-gentle-introduction/ch01s02_html), Raúl Garreta; Guillermo Moncecchi, Packt Publishing, 2013.\n",
"* [Python Machine Learning](http://proquest.safaribooksonline.com/book/programming/python/9781783555130), Sebastian Raschka, Packt Publishing, 2015.\n",
"* [Plot the decision surface of a decision tree on the iris dataset](https://scikit-learn.org/stable/auto_examples/tree/plot_iris_dtc.html)\n",
"* [scikit-learn : Machine Learning Simplified](https://learning.oreilly.com/library/view/scikit-learn-machine/9781788833479/), Raúl Garreta; Guillermo Moncecchi, Packt Publishing, 2017.\n",
"* [Python Machine Learning](https://learning.oreilly.com/library/view/python-machine-learning/9781789955750/), Sebastian Raschka, Packt Publishing, 2019.\n",
"* [Parameter estimation using grid search with cross-validation](http://scikit-learn.org/stable/auto_examples/model_selection/grid_search_digits.html)\n",
"* [Decision trees in python with scikit-learn and pandas](http://chrisstrelioff.ws/sandbox/2015/06/08/decision_trees_in_python_with_scikit_learn_and_pandas.html)"
]
@ -543,7 +543,7 @@
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
@ -557,7 +557,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.6"
"version": "3.8.12"
},
"latex_envs": {
"LaTeX_envs_menu_present": true,

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