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Add python notebooks for Associate wrap-up quizzes
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‎notebooks/overfit_wrap_up_ex_00.ipynb‎

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{
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"cells": [
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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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"# \ud83c\udfc1 Wrap-up quiz 2\n",
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"\n",
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"This notebook contains the guided project to answer the hands-on questions\n",
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"corresponding to the module \"Selecting the best model\" of the Associate\n",
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"Practitioner Course. In this test **we do not have access to your code**. Only\n",
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"it's output is evaluated by using the multiple choice questions, to be\n",
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"answered in the dedicated User Interface.\n",
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"\n",
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"First run the following cell to initialize jupyterlite. Notice that only basic\n",
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"libraries are available, such as pandas, matplotlib, seaborn and numpy.\n",
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"Remember that the initial import of libraries can take longer than usual, it\n",
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"may take around 10-20 seconds for the following cell to run. Please be\n",
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"patient."
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]
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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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"metadata": {},
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"outputs": [],
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"source": [
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"%pip install seaborn==0.13.2\n",
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"import matplotlib\n",
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"import numpy\n",
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"import pandas\n",
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"import seaborn\n",
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"import sklearn"
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]
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},
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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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"Load the `blood_transfusion.csv` dataset with the following cell of code. The\n",
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"column \"Class\" contains the target variable."
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]
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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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"metadata": {},
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"\n",
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"blood_transfusion = pd.read_csv(\"../datasets/blood_transfusion.csv\")\n",
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"target_name = \"Class\"\n",
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"data = blood_transfusion.drop(columns=target_name)\n",
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"target = blood_transfusion[target_name]"
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]
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},
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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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"Select the correct answers from the following proposals.\n",
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"\n",
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"- a) The problem to be solved is a regression problem\n",
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"- b) The problem to be solved is a binary classification problem (exactly 2\n",
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" possible classes)\n",
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"- c) The problem to be solved is a multiclass classification problem (more\n",
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" than 2 possible classes)\n",
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"- d) The proportions of the class counts are imbalanced: some classes have\n",
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" more than twice as many rows than others\n",
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"\n",
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"_Select all answers that apply_\n",
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"\n",
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"Hint: `target.unique()` and `target.value_counts()` are helpful methods to\n",
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"answer this question."
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]
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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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"metadata": {},
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"outputs": [],
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"source": [
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"# Write your code here."
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]
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},
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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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"Using a\n",
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"[`sklearn.dummy.DummyClassifier`](https://scikit-learn.org/stable/modules/generated/sklearn.dummy.DummyClassifier.html)\n",
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"and the strategy `\"most_frequent\"`, what is the average of the accuracy scores\n",
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"obtained by performing a 10-fold cross-validation?\n",
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"\n",
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"- a) ~25%\n",
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"- b) ~50%\n",
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"- c) ~75%\n",
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"\n",
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"_Select a single answer_\n",
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"\n",
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"Hint: You can check the documentation of\n",
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"[`sklearn.model_selection.cross_val_score`](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.cross_val_score.html)\n",
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"and\n",
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"[`sklearn.model_selection.cross_validate`](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.cross_validate.html)."
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]
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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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"metadata": {},
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"outputs": [],
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"source": [
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"# Write your code here."
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]
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},
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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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"Repeat the previous experiment but compute the balanced accuracy instead of\n",
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"the accuracy score. Pass `scoring=\"balanced_accuracy\"` when calling\n",
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"`cross_validate` or `cross_val_score` functions, the mean score is:\n",
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"\n",
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"- a) ~25%\n",
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"- b) ~50%\n",
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"- c) ~75%\n",
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"\n",
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"_Select a single answer_"
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]
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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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"metadata": {},
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"outputs": [],
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"source": [
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"# Write your code here."
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]
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},
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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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"We will use a `sklearn.neighbors.KNeighborsClassifier` for the remainder of this quiz.\n",
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"\n",
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"Why is it relevant to add a preprocessing step to scale the data using a\n",
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"`StandardScaler` when working with a `KNeighborsClassifier`?\n",
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"\n",
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"- a) faster to compute the list of neighbors on scaled data\n",
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"- b) k-nearest neighbors is based on computing some distances. Features need\n",
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" to be normalized to contribute approximately equally to the distance\n",
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" computation.\n",
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"- c) This is irrelevant. One could use k-nearest neighbors without normalizing\n",
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" the dataset and get a very similar cross-validation score.\n",
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"\n",
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"_Select a single answer_"
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]
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},
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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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"# Create a scikit-learn pipeline (using\n",
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"`sklearn.pipeline.make_pipeline`) where a StandardScaler will be used to scale\n",
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"the data followed by a KNeighborsClassifier. Use the default hyperparameters.\n",
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"\n",
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"Inspect the parameters of the created pipeline. What is the value of K, the\n",
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"number of neighbors considered when predicting with the k-nearest neighbors.\n",
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"\n",
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"- a) 1\n",
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"- b) 3\n",
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"- c) 5\n",
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"- d) 8\n",
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"- e) 10\n",
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"\n",
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"_Select a single answer_\n",
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"\n",
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"Hint: You can use `model.get_params()` to get the parameters of a scikit-learn\n",
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"estimator."
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]
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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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"metadata": {},
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"outputs": [],
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"source": [
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"# Write your code here."
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]
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},
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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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"# Set `n_neighbors=1` in the previous model and evaluate it using a 10-fold\n",
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"cross-validation. Use the balanced accuracy as a score. What can you say about\n",
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"this model? Compare the average of the train and test scores to argument your\n",
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"answer.\n",
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"\n",
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"- a) The model underfits\n",
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"- b) The model generalizes\n",
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"- c) The model overfits\n",
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"\n",
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"_Select a single answer_\n",
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"\n",
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"Hint: compute the average test score and the average train score and compare\n",
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"them. Make sure to pass `return_train_score=True` to the `cross_validate`\n",
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"function to also compute the train score."
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]
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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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"metadata": {},
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"outputs": [],
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"source": [
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"# Write your code here."
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]
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},
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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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"We now study the effect of the parameter n_neighbors on the train and test\n",
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"score using a validation curve. You can use the following parameter range:"
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]
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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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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"\n",
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"param_range = np.array([1, 2, 5, 10, 20, 50, 100, 200, 500])"
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]
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},
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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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"Also, use a 5-fold cross-validation and compute the balanced accuracy score\n",
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"instead of the default accuracy score (check the scoring parameter). Finally,\n",
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"plot the average train and test scores for the different value of the\n",
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"hyperparameter. Remember that the name of the parameter can be found using\n",
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"`model.get_params()`.\n",
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"\n",
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"Select the true affirmations stated below:\n",
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"\n",
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"- a) The model underfits for a range of `n_neighbors` values between 1 to 10\n",
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"- b) The model underfits for a range of `n_neighbors` values between 10 to 100\n",
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"- c) The model underfits for a range of `n_neighbors` values between 100 to 500\n",
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"\n",
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"_Select a single answer_"
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]
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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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"metadata": {},
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"outputs": [],
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"source": [
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"# Write your code here."
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]
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},
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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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"Select the most correct of the affirmations stated below:\n",
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"\n",
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"- a) The model overfits for a range of `n_neighbors` values between 1 to 10\n",
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"- b) The model overfits for a range of `n_neighbors` values between 10 to 100\n",
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"- c) The model overfits for a range of `n_neighbors` values between 100 to 500\n",
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"\n",
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"_Select a single answer_"
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]
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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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"metadata": {},
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"outputs": [],
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"source": [
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"# Select the most correct of the affirmations stated below:\n",
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"#\n",
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"# - a) The model best generalizes for a range of `n_neighbors` values between 1 to 10\n",
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"# - b) The model best generalizes for a range of `n_neighbors` values between 10 to 100\n",
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"# - c) The model best generalizes for a range of `n_neighbors` values between 100 to 500\n",
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"#\n",
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"# _Select a single answer_"
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]
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}
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],
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"metadata": {
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"jupytext": {
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"main_language": "python"
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},
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"kernelspec": {
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"display_name": "Python 3",
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"name": "python3"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}

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