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Code Issues Releases Wiki Activity

Updated notebooks

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This commit is contained in:
Carlos A. Iglesias 2019-03-06 17:44:30 +01:00
parent d4b59e702d
commit 762157bfe1
3 changed files with 664 additions and 64 deletions
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442
ml1/2_3_1_Advanced_Visualisation.ipynb
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ml1/2_5_1_kNN_Model.ipynb
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ml1/2_5_2_Decision_Tree_Model.ipynb
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@ -74,7 +74,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 1,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -124,9 +124,25 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 2,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [
{
"data": {
"text/plain": [
"DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=3,\n",
" max_features=None, max_leaf_nodes=None,\n",
" min_impurity_decrease=0.0, min_impurity_split=None,\n",
" min_samples_leaf=1, min_samples_split=2,\n",
" min_weight_fraction_leaf=0.0, presort=False, random_state=1,\n",
" splitter='best')"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [ "source": [
"from sklearn.tree import DecisionTreeClassifier\n", "from sklearn.tree import DecisionTreeClassifier\n",
"import numpy as np\n", "import numpy as np\n",
@ -145,9 +161,24 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 3,
"metadata": {}, "metadata": {},
"outputs": [], "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": [ "source": [
"print(\"Prediction \", model.predict(x_train))\n", "print(\"Prediction \", model.predict(x_train))\n",
"print(\"Expected \", y_train)" "print(\"Expected \", y_train)"
@ -162,9 +193,26 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 4,
"metadata": {}, "metadata": {},
"outputs": [], "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": [ "source": [
"# Print the \n", "# Print the \n",
"print(\"Predicted probabilities\", model.predict_proba(x_train[:10]))" "print(\"Predicted probabilities\", model.predict_proba(x_train[:10]))"
@ -172,9 +220,17 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 5,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Accuracy in training 0.9821428571428571\n"
]
}
],
"source": [ "source": [
"# Evaluate Accuracy in training\n", "# Evaluate Accuracy in training\n",
"\n", "\n",
@ -185,9 +241,17 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 6,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Accuracy in testing 0.9210526315789473\n"
]
}
],
"source": [ "source": [
"# Now we evaluate error in testing\n", "# Now we evaluate error in testing\n",
"y_test_pred = model.predict(x_test)\n", "y_test_pred = model.predict(x_test)\n",
@ -209,9 +273,21 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 7,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [
{
"ename": "ModuleNotFoundError",
"evalue": "No module named 'pydotplus'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-7-1bf5ec7fb043>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mIPython\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdisplay\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mImage\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexternals\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msix\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mStringIO\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mpydotplus\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mpydot\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mdot_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mStringIO\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;31mModuleNotFoundError\u001b[0m: No module named 'pydotplus'"
]
}
],
"source": [ "source": [
"from IPython.display import Image \n", "from IPython.display import Image \n",
"from sklearn.externals.six import StringIO\n", "from sklearn.externals.six import StringIO\n",
@ -468,7 +544,7 @@
"name": "python", "name": "python",
"nbconvert_exporter": "python", "nbconvert_exporter": "python",
"pygments_lexer": "ipython3", "pygments_lexer": "ipython3",
"version": "3.6.7" "version": "3.7.1"
}, },
"latex_envs": { "latex_envs": {
"LaTeX_envs_menu_present": true, "LaTeX_envs_menu_present": true,