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quizzes as pdfs and workbook

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2021-10-23 22:31:51 +02:00
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85
notebooks/6_analyze_toydata.ipynb
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@@ -256,7 +256,7 @@
"feature_cols = [\"product_1\", \"product_5\", \"product_17\", \"height\", \"width\", \"depth\"]\n",
"X = df[feature_cols].to_numpy() # convert df into a numpy array\n",
"# ... and the vector with labels\n",
"y = df[[\"faulty\"]].to_numpy()\n",
"y = df[\"faulty\"].to_numpy()\n",
"# to evaluate our prediction model, we need to split off a test dataset\n",
"# later we will use the train_test_split function from sklearn to do this, \n",
"# but this just goes to show that there is no magic behind it\n",
@@ -340,46 +340,12 @@
"metadata": {},
"outputs": [],
"source": [
"# maybe we just need to give the tree the freedom to make more splits? (i.e., increase its depth)\n",
"clf = tree.DecisionTreeClassifier(max_depth=100, random_state=1)\n",
"clf = clf.fit(X_train, y_train)\n",
"print(f\"Accuracy on training data: {clf.score(X_train, y_train):.3f}\")\n",
"print(f\"Accuracy on test data: {clf.score(X_test, y_test):.3f}\")\n",
"print(f\"Balanced accuracy on training data: {balanced_accuracy_score(y_train, clf.predict(X_train)):.3f}\")\n",
"print(f\"Balanced accuracy on test data: {balanced_accuracy_score(y_test, clf.predict(X_test)):.3f}\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Questions:** \\\n",
"Is this a better model? If anything, is the model over- or underfitting?"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# when the tree is too large (or you're using a random forest),\n",
"# check the feature importances instead of plotting the tree\n",
"dict(zip(feature_cols, clf.feature_importances_))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# now let's do what we probably should have done in the beginning and \n",
"# let's do what we probably should have done in the beginning and \n",
"# remove the outliers (i.e., keep only samples with a height > 0)\n",
"df_new = df[df[\"height\"] > 0.]\n",
"# create a train/test split again, this time using the sklearn function\n",
"X_train, X_test, y_train, y_test = train_test_split(df_new[feature_cols].to_numpy(), \n",
" df_new[[\"faulty\"]].to_numpy(), \n",
" df_new[\"faulty\"].to_numpy(), \n",
" test_size=0.33, random_state=15)\n",
"# see how imbalanced the label distribution in the training and test sets is\n",
"print(f\"Fraction of ok items in training set: {1-np.mean(y_train):.3f}\")\n",
@@ -408,6 +374,18 @@
"print(f\"Balanced accuracy on test data: {balanced_accuracy_score(y_test, clf.predict(X_test)):.3f}\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# plot the tree again\n",
"plt.figure(figsize=(15, 10))\n",
"tree.plot_tree(clf, feature_names=feature_cols, filled=True, class_names=np.array(clf.classes_, dtype=str), proportion=True);\n",
"# notice how in the leaf nodes where the tree predicts \"faulty\", there are only very few data points"
]
},
{
"cell_type": "markdown",
"metadata": {},
@@ -422,10 +400,32 @@
"metadata": {},
"outputs": [],
"source": [
"# plot the tree\n",
"plt.figure(figsize=(15, 10))\n",
"tree.plot_tree(clf, feature_names=feature_cols, filled=True, class_names=np.array(clf.classes_, dtype=str), proportion=True);\n",
"# notice how in the leaf nodes where the tree predicts \"faulty\", there are only very few data points"
"# maybe we just need to give the tree the freedom to make more splits? (i.e., increase its depth)\n",
"clf = tree.DecisionTreeClassifier(max_depth=100, random_state=1)\n",
"clf = clf.fit(X_train, y_train)\n",
"print(f\"Accuracy on training data: {clf.score(X_train, y_train):.3f}\")\n",
"print(f\"Accuracy on test data: {clf.score(X_test, y_test):.3f}\")\n",
"print(f\"Balanced accuracy on training data: {balanced_accuracy_score(y_train, clf.predict(X_train)):.3f}\")\n",
"print(f\"Balanced accuracy on test data: {balanced_accuracy_score(y_test, clf.predict(X_test)):.3f}\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Questions:** \\\n",
"Is this a better model? If anything, is the model over- or underfitting?"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# when the tree is too large (or you're using a random forest),\n",
"# check the feature importances instead of plotting the tree\n",
"dict(zip(feature_cols, clf.feature_importances_))"
]
},
{
@@ -521,7 +521,7 @@
"# let's try with temp as an additional feature\n",
"feature_cols = [\"product_1\", \"product_5\", \"product_17\", \"height\", \"width\", \"depth\", \"temp\"]\n",
"X = df_new[feature_cols].to_numpy()\n",
"y = df_new[[\"faulty\"]].to_numpy()\n",
"y = df_new[\"faulty\"].to_numpy()\n",
"# split into train/test sets again\n",
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=15)\n",
"# see how imbalanced the label distribution in the training and test sets is\n",
@@ -706,7 +706,6 @@
"outputs": [],
"source": [
"# try a different classifier: logistic regression\n",
"y_train, y_test = y_train.flatten(), y_test.flatten() # otherwise the model will complain about the shapes\n",
"# first, try the model with the default parameter settings\n",
"clf = LogisticRegression()\n",
"clf = clf.fit(X_train, y_train)\n",

2
notebooks/7_hard_drive_failures.ipynb
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@@ -117,7 +117,7 @@
"metadata": {},
"source": [
"-------------------------------------------------------------------------------------\n",
"You're already given this rudimentary prediction pipeline, now your job is to improve it. Below are some things you might want to try, but feel free to get creative! Have a look at the [cheat sheet](https://github.com/cod3licious/ml_exercises/blob/main/cheatsheet.pdf) for more ideas and a concise overview of the relevant steps when developing a machine learning solution in any data science project. \n",
"You're already given this rudimentary prediction pipeline, now your job is to improve it. Below are some things you might want to try, but feel free to get creative! Have a look at the [cheat sheet](https://github.com/cod3licious/ml_exercises/blob/main/other/cheatsheet.pdf) for more ideas and a concise overview of the relevant steps when developing a machine learning solution in any data science project. \n",
"\n",
"The previous notebook, \"analyze toydata\", deals with a very similar problem and can serve as a guideline for this exercise. For an example of how to use the t-SNE algorithm, have a look at the first notebook, \"visualize text\" (but please note that since you don't have sparse data here, there is no need to transform the data with a kernel PCA before using t-SNE).\n",
"\n",