brospars/JupyterNotebookGuide.ipynb

## JupyterNotebookGuide.ipynb
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Jupyter Notebooks Guide"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The course material of this Mooc is proposed in **Jupyter notebooks** which are documents that contain live code, equations, visualizations and narrative text. To manipulate these notebooks, the open-source web application Jupyter has been integrated into this Mooc. It does not require any installation on your side. All data and calculations will be hosted on our servers.  \n",
    "This document is an example of a Jupyter Notebook. It presents the Jupyter interface and how to use the notebooks.\n",
    "\n",
    "Please note that **code parts can be evaluated and modified**. So you can easily try out variants around the original notebook. Do not hesitate since you will be able to reset to the original version of the notebook.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Table of content\n",
    "- How to use Jupyter Notebooks\n",
    "- How to restart the kernel \n",
    "- Shutdown running notebooks\n",
    "- Basic shortcuts\n",
    "- References about Jupyter notebook\n",
    "- Installing Jupyter on your computer\n",
    "  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## How to use Jupyter Notebooks\n",
    "### Executing Python code"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "At the top of the notebook, you have a menu bar, containing:\n",
    "\n",
    "* a menu bar with the entries `File`, `Edit`, `Insert`, `Cell`, `Kernel`;\n",
    "* and a button bar that are shortcuts to some frequently used menus. If you leave your mouse over a button, a small text will appear, indicating what function the button is for.\n",
    "\n",
    "A notebook consists of a series of cells, either textual or containing code. The code cells are easily recognisable, they are preceded by `In [ ]:`. The cell following the one you are reading is a code cell.\n",
    "\n",
    "To start, select this code cell below with your mouse (the line preceded by `In [ ]:`), and press in the menu bar the \"Run button\" (the one surrounded by a red circle):"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\" 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\"/>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "20 * 30"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, the cell is \"executed\" (more commonly called evaluated), and we move on to the next cell.  \n",
    "When a code cell has been evaluated, Jupyter adds under the cell `In` a cell `Out` which gives the result of the Python code, above `600`.\n",
    "\n",
    "Jupyter also adds a number between the square brackets to display, for example above, `In [1]:` and `Out [1]:`. This number allows you to retrieve the order in which the cells were evaluated.\n",
    "\n",
    "Alternatively, you can simply type on the keyboard ***Shift+Enter***,  to get the same effect. In general, it is important to learn and use the keyboard shortcuts, this will save you a lot of time later."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you want to *execute* the entire notebook step by step, you have to:\n",
    "\n",
    "* select each cell of the notebook,\n",
    "* and press ***Shift+Enter*** until the notebook is finished."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is also possible to evaluate the whole notebook at once by using the menu *Cell -> Run All*."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can of course modify these code cells for testing purposes, so you can use the model below to calculate the square root of 2, or try the function on a negative number and see how the error is reported."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# math.sqrt (for square root) calculates the square root\n",
    "import math\n",
    "math.sqrt(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Be careful to evaluate the cells in the right order"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is important that the code cells are evaluated in the correct order. If you do not respect the order in which the code cells are presented, the result may be unexpected.\n",
    "\n",
    "In fact, evaluating a programme in the form of a notebook is like cutting it up into small fragments, and if these fragments are executed in a disorderly manner, the result is naturally a different programme."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This can be seen in this example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "message = \"Be careful about the order in which you evaluate the notebooks.\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(message)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If further on the notebook we have the following cell:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# this has the effect of deleting the variable 'message'.\n",
    "del message"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "which makes the symbol `message` indefinite, then of course we can no longer evaluate the cell which makes `print` since the variable `message` is no longer known to the interpreter."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### You are working on a copy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One of the main advantage of notebooks is that you can modify the code we have written, and see for yourself how the modified code behaves.\n",
    "\n",
    "For this reason, each student has his/her **own copy** of each notebook, you can of course make any changes you wish to your notebooks without affecting other students."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Back to the original the course version"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can always return to the \"course\" version through the *File → Reset to original* menu.  \n",
    "Be careful, with this function you restore **the whole notebook** and therefore **you loose your modifications on it**.\n",
    "\n",
    "Note the *File → Reset all to original* menu allows you to **reset to original version all the notebooks of the Mooc**. If you use it, it takes a few minutes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Download in other formats"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can download a notebook in Python, pdf, html... format to your computer through the menu\n",
    "*File → Download as → Python*  \n",
    "For the Python download, text cells are preserved in the result as Python comments."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Adding cells"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can add a cell anywhere in the document with the **+** button on the button bar (or with the menu *Insert → Insert Cell Above* or *Insert → Insert Cell Below* ).  \n",
    "Also, when you reach the end of the document, a new cell is created each time you evaluate the last cell; this way you have a draft for your own tests.  \n",
    "\n",
    "When you create a new cell, you have to select its type between Markdown, Python.  \n",
    "**Markdown is lightweight markup languages**. Github proposes a short and efficient introduction: <a href=\"https://guides.github.com/features/mastering-markdown/\">Mastering Markdown</a>."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## How to restart the kernel\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you made too many changes, or loose track of what you have evaluated, it may be useful to restart your interpreter:\n",
    "* The *Kernel → Restart* menu allows you to do this, a bit like IDLE which starts from a blank interpreter when you use the F5 function.\n",
    "* The *Kernel → Restart and Clear Output* menu restarts the kernel and clears all the outputs.\n",
    "* The *Kernel → Interrupt* menu can be used if your fragment takes too long to run (e.g. you have written a loop whose logic is broken and does not finish).\n",
    "* The *Kernel → shutdown* menu can be used to close a running notebook if you need to free memory for exemple."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Shutdown running notebooks\n",
    "The *File → Running notebooks* menu displays the running notebooks.\n",
    "Then Click on each *Shutdown* button to stop the process. It may be very usefull if you have opened too many notebooks. \n",
    "<img src=\" data:image/png;base64,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\" width=500/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Basic shortcuts\n",
    "All actions in the notebook can be performed with the mouse, but keyboard shortcuts are also available for the most common ones. The essential shortcuts to remember are the following:\n",
    "- Run one cell and go to the next one → *Shift + Enter*\n",
    "- Run one cell and stay on the same cell → *Ctrl + Enter*\n",
    "- Go from Edit mode (Green cell) to Select mode (Blue cell) → *Escape*\n",
    "- Go from Select mode (Blue cell) to Edit mode (Green cell) → *Enter*\n",
    "- Add a cell below the current cell → *Select mode + \"b\"*\n",
    "- Add a cell above the current cell → *Select mode + \"a\"*\n",
    "- Delete a cell → *Select mode + \"dd\"*\n",
    "- Cut/Copy/Paste → *Select mode + \"x\"/\"c\"/\"v\"*\n",
    "- Convert a cell from code to markdown → *Select mode + \"m\"*\n",
    "- Convert a cell from mardown to cell → *Select mode + \"y\"*\n",
    "- Selecting several cells → *Select mode + Shift + arrow up/down*\n",
    "- and many others ...\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## References about Jupyter notebook\n",
    "\n",
    "There are many documentations and tutorials about Jupyter notebooks, here are a few of them:\n",
    "* This [web page on dataquest blog](https://www.dataquest.io/blog/jupyter-notebook-tips-tricks-shortcuts/) contains a number of tips on how to use Jupyter (including illustrations of the many IPython magic commands) that can improve your efficiency.\n",
    "* The [Jupyter Notebook documentation](https://jupyter-notebook.readthedocs.io/en/stable/notebook.html) is also very usefull.\n",
    "* This [Youtube playlist](https://www.youtube.com/playlist?list=PLRJx8WOUx5XcDMOxSQegCJUjTJePTlF9Z) contains many tutorials on the use of notebooks."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Installing Jupyter on your computer\n",
    "If you prefer to work in standalone mode, you may install Jupyter on your computer.\n",
    "\n",
    "To proceed with the installation of all dependencies for the course (not only scikit-learn), you can have a look at the [local installation page](https://github.com/INRIA/scikit-learn-mooc/blob/master/local-install-instructions.md).\n",
    "\n",
    "At last, you will find all the **notebooks and datasets files in the Mooc file github repository**: [https://github.com/INRIA/scikit-learn-mooc](https://github.com/INRIA/scikit-learn-mooc).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now it's up to you."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "jupytext": {
   "cell_metadata_filter": "all",
   "notebook_metadata_filter": "all,-language_info,-toc,-jupytext.text_representation.jupytext_version,-jupytext.text_representation.format_version",
   "text_representation": {
    "extension": ".md",
    "format_name": "myst"
   }
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.6"
  },
  "nbreset": "https://gitlab.inria.fr/learninglab/mooc-scikit-learn/mooc-scikit-learn-model/raw/master/notebook/JupyterNotebookGuide.ipynb"
 },
 "nbformat": 4,
 "nbformat_minor": 4
}

## notebooks.json
{
  "baseUrl": "https://github.com/INRIA/scikit-learn-mooc/raw/main/",
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    "notebooks/02_numerical_pipeline_cross_validation.ipynb",
    "notebooks/02_numerical_pipeline_ex_00.ipynb",
    "notebooks/02_numerical_pipeline_ex_01.ipynb",
    "notebooks/02_numerical_pipeline_hands_on.ipynb",
    "notebooks/02_numerical_pipeline_introduction.ipynb",
    "notebooks/02_numerical_pipeline_scaling.ipynb",
    "notebooks/03_categorical_pipeline.ipynb",
    "notebooks/03_categorical_pipeline_column_transformer.ipynb",
    "notebooks/03_categorical_pipeline_ex_01.ipynb",
    "notebooks/03_categorical_pipeline_ex_02.ipynb",
    "notebooks/cross_validation_baseline.ipynb",
    "notebooks/cross_validation_ex_01.ipynb",
    "notebooks/cross_validation_ex_02.ipynb",
    "notebooks/cross_validation_ex_03.ipynb",
    "notebooks/cross_validation_ex_04.ipynb",
    "notebooks/cross_validation_ex_05.ipynb",
    "notebooks/cross_validation_grouping.ipynb",
    "notebooks/cross_validation_learning_curve.ipynb",
    "notebooks/cross_validation_nested.ipynb",
    "notebooks/cross_validation_stratification.ipynb",
    "notebooks/cross_validation_time.ipynb",
    "notebooks/cross_validation_train_test.ipynb",
    "notebooks/cross_validation_validation_curve.ipynb",
    "notebooks/datasets_ames_housing.ipynb",
    "notebooks/datasets_bike_rides.ipynb",
    "notebooks/datasets_blood_transfusion.ipynb",
    "notebooks/datasets_california_housing.ipynb",
    "notebooks/dev_features_importance.ipynb",
    "notebooks/ensemble_adaboost.ipynb",
    "notebooks/ensemble_bagging.ipynb",
    "notebooks/ensemble_ex_01.ipynb",
    "notebooks/ensemble_ex_02.ipynb",
    "notebooks/ensemble_ex_03.ipynb",
    "notebooks/ensemble_ex_04.ipynb",
    "notebooks/ensemble_ex_05.ipynb",
    "notebooks/ensemble_gradient_boosting.ipynb",
    "notebooks/ensemble_hist_gradient_boosting.ipynb",
    "notebooks/ensemble_hyperparameters.ipynb",
    "notebooks/ensemble_introduction.ipynb",
    "notebooks/ensemble_random_forest.ipynb",
    "notebooks/feature_selection_ex_01.ipynb",
    "notebooks/feature_selection_introduction.ipynb",
    "notebooks/feature_selection_limitation_model.ipynb",
    "notebooks/helpers/__init__.py",
    "notebooks/helpers/plotting.py",
    "notebooks/linear_models_ex_01.ipynb",
    "notebooks/linear_models_ex_02.ipynb",
    "notebooks/linear_models_ex_03.ipynb",
    "notebooks/linear_models_ex_04.ipynb",
    "notebooks/linear_models_ex_05.ipynb",
    "notebooks/linear_models_regularization.ipynb",
    "notebooks/linear_regression_in_sklearn.ipynb",
    "notebooks/linear_regression_non_linear_link.ipynb",
    "notebooks/linear_regression_without_sklearn.ipynb",
    "notebooks/logistic_regression.ipynb",
    "notebooks/logistic_regression_non_linear.ipynb",
    "notebooks/matplotlibrc",
    "notebooks/metrics_classification.ipynb",
    "notebooks/metrics_ex_01.ipynb",
    "notebooks/metrics_ex_02.ipynb",
    "notebooks/metrics_regression.ipynb",
    "notebooks/parameter_tuning_ex_02.ipynb",
    "notebooks/parameter_tuning_ex_03.ipynb",
    "notebooks/parameter_tuning_grid_search.ipynb",
    "notebooks/parameter_tuning_manual.ipynb",
    "notebooks/parameter_tuning_nested.ipynb",
    "notebooks/parameter_tuning_parallel_plot.ipynb",
    "notebooks/parameter_tuning_randomized_search.ipynb",
    "notebooks/trees_classification.ipynb",
    "notebooks/trees_dataset.ipynb",
    "notebooks/trees_ex_01.ipynb",
    "notebooks/trees_ex_02.ipynb",
    "notebooks/trees_hyperparameters.ipynb",
    "notebooks/trees_regression.ipynb",
    "notebooks/video_pipeline.ipynb"
  ],
  "datasets": [
    "datasets/README.md",
    "datasets/adult-census-numeric-all.csv",
    "datasets/adult-census-numeric-test.csv",
    "datasets/adult-census-numeric.csv",
    "datasets/adult-census.csv",
    "datasets/ames_housing_no_missing.csv",
    "datasets/bike_rides.csv",
    "datasets/blood_transfusion.csv",
    "datasets/cps_85_wages.csv",
    "datasets/financial-data",
    "datasets/house_prices.csv",
    "datasets/penguins.csv",
    "datasets/penguins_classification.csv",
    "datasets/penguins_regression.csv"
  ],
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    "evaluation/linear_models_questions.ipynb",
    "evaluation/trees_questions.ipynb",
    "evaluation/predictive_modeling_pipeline_questions.ipynb",
    "evaluation/overfit_questions.ipynb",
    "evaluation/tuning_questions.ipynb",
    "evaluation/evaluation_questions.ipynb",
    "evaluation/sandbox_notebook.ipynb"
  ],
  "figures": [
    "figures/README.md",
    "figures/api_diagram-columntransformer.svg",
    "figures/api_diagram-pipeline.fit.svg",
    "figures/api_diagram-pipeline.predict.svg",
    "figures/api_diagram-predictor.fit.svg",
    "figures/api_diagram-predictor.predict.svg",
    "figures/api_diagram-predictor.score.svg",
    "figures/api_diagram-transformer.fit.svg",
    "figures/api_diagram-transformer.fit_transform.svg",
    "figures/api_diagram-transformer.transform.svg",
    "figures/api_diagram.drawio",
    "figures/bagging.svg",
    "figures/bagging0.svg",
    "figures/bagging0_cross.svg",
    "figures/bagging_cross.svg",
    "figures/bagging_fit.svg",
    "figures/bagging_line.svg",
    "figures/bagging_overfit.svg",
    "figures/bagging_reg_blue.svg",
    "figures/bagging_reg_blue_grey.svg",
    "figures/bagging_reg_data.svg",
    "figures/bagging_reg_grey.svg",
    "figures/bagging_reg_grey_fitted.svg",
    "figures/bagging_trees.svg",
    "figures/bagging_trees_predict.svg",
    "figures/bagging_underfit.svg",
    "figures/bagging_vote.svg",
    "figures/boosting",
    "figures/boosting0.svg",
    "figures/boosting0_cross.svg",
    "figures/boosting1.svg",
    "figures/boosting2.svg",
    "figures/boosting3.svg",
    "figures/boosting_reg_blue.svg",
    "figures/boosting_reg_data.svg",
    "figures/boosting_reg_grey.svg",
    "figures/boosting_trees1.svg",
    "figures/boosting_trees2.svg",
    "figures/boosting_trees3.svg",
    "figures/bossting_reg_blue.svg",
    "figures/categorical.svg",
    "figures/cross-val1-shuffle.svg",
    "figures/cross-val1.svg",
    "figures/cross-val2-shuffle.svg",
    "figures/cross-val2.svg",
    "figures/cross-val3-shuffle.svg",
    "figures/cross-val3.svg",
    "figures/cross-val4-shuffle.svg",
    "figures/cross-val4.svg",
    "figures/cross-val5-shuffle.svg",
    "figures/cross-val5.svg",
    "figures/cross_validation_diagram.png",
    "figures/cross_validation_train_test_diagram.png",
    "figures/different_models_complex_16.svg",
    "figures/different_models_complex_4.svg",
    "figures/dt_fit.svg",
    "figures/dt_overfit.svg",
    "figures/dt_underfit.svg",
    "figures/evaluation_quiz_precision_recall_C0.003.svg",
    "figures/evaluation_quiz_precision_recall_C1.svg",
    "figures/full_data.svg",
    "figures/grid_vs_random_search.svg",
    "figures/iris-silhouette.svg",
    "figures/iris-silhouette_gray.svg",
    "figures/iris_petal_length_cm_hist.svg",
    "figures/iris_petal_width_cm_hist.svg",
    "figures/iris_sepal_length_cm_hist.svg",
    "figures/iris_sepal_width_cm_hist.svg",
    "figures/legend_irises.svg",
    "figures/lin_not_separable.svg",
    "figures/lin_reg_2_points.svg",
    "figures/lin_reg_2_points_best_ridge.svg",
    "figures/lin_reg_2_points_best_ridge_grey.svg",
    "figures/lin_reg_2_points_no_penalty.svg",
    "figures/lin_reg_2_points_no_penalty_grey.svg",
    "figures/lin_reg_2_points_ridge.svg",
    "figures/lin_reg_2_points_ridge_grey.svg",
    "figures/lin_reg_3D.svg",
    "figures/lin_separable.svg",
    "figures/linear_data.svg",
    "figures/linear_fit.svg",
    "figures/linear_fit_red.svg",
    "figures/linear_ols.svg",
    "figures/linear_ols_test.svg",
    "figures/linear_splines.svg",
    "figures/linear_splines_test.svg",
    "figures/linreg_noreg_0.svg",
    "figures/linreg_noreg_0_nogrey.svg",
    "figures/linreg_noreg_1.svg",
    "figures/linreg_noreg_2.svg",
    "figures/linreg_noreg_3.svg",
    "figures/linreg_noreg_4.svg",
    "figures/linreg_noreg_5.svg",
    "figures/logistic_2D.svg",
    "figures/logistic_2D_C0.001.svg",
    "figures/logistic_2D_C1.svg",
    "figures/logistic_3D.svg",
    "figures/logistic_color.svg",
    "figures/mooc_computer.jpg",
    "figures/multinomial.svg",
    "figures/nested_cross_validation_diagram.png",
    "figures/numerical_pipeline_quiz_scaler.py",
    "figures/numerical_pipeline_quiz_scaler_original.png",
    "figures/numerical_pipeline_quiz_scaler_preprocessing.png",
    "figures/ols_simple.svg",
    "figures/ols_simple_test.svg",
    "figures/ols_test.svg",
    "figures/people.svg",
    "figures/plot_cross_validation_diagram.py",
    "figures/plot_iris_visualization.py",
    "figures/plot_overfit_underfit.py",
    "figures/plot_parameter_tuning_cv.py",
    "figures/plot_precision_recall_visualization.py",
    "figures/plot_simple_decision_tree_adult_census.py",
    "figures/plot_slide_linear.py",
    "figures/plot_splines.py",
    "figures/plot_trees.py",
    "figures/polynomial_learning_curve_1179.svg",
    "figures/polynomial_learning_curve_145.svg",
    "figures/polynomial_learning_curve_42.svg",
    "figures/polynomial_learning_curve_6766.svg",
    "figures/polynomial_overfit.svg",
    "figures/polynomial_overfit_0.svg",
    "figures/polynomial_overfit_1.svg",
    "figures/polynomial_overfit_2.svg",
    "figures/polynomial_overfit_5.svg",
    "figures/polynomial_overfit_9.svg",
    "figures/polynomial_overfit_assymptotic.svg",
    "figures/polynomial_overfit_ntrain_1179.svg",
    "figures/polynomial_overfit_ntrain_145.svg",
    "figures/polynomial_overfit_ntrain_42.svg",
    "figures/polynomial_overfit_ntrain_6766.svg",
    "figures/polynomial_overfit_resample_0.svg",
    "figures/polynomial_overfit_resample_1.svg",
    "figures/polynomial_overfit_resample_2.svg",
    "figures/polynomial_overfit_resample_all.svg",
    "figures/polynomial_overfit_simple.svg",
    "figures/polynomial_overfit_simple_legend.svg",
    "figures/polynomial_overfit_test_1.svg",
    "figures/polynomial_overfit_test_2.svg",
    "figures/polynomial_overfit_test_5.svg",
    "figures/polynomial_overfit_test_9.svg",
    "figures/polynomial_overfit_truth.svg",
    "figures/polynomial_underfit_resample_0.svg",
    "figures/polynomial_underfit_resample_1.svg",
    "figures/polynomial_underfit_resample_2.svg",
    "figures/polynomial_underfit_resample_all.svg",
    "figures/polynomial_underfit_simple.svg",
    "figures/polynomial_validation_curve.svg",
    "figures/polynomial_validation_curve_1.svg",
    "figures/polynomial_validation_curve_15.svg",
    "figures/polynomial_validation_curve_2.svg",
    "figures/polynomial_validation_curve_5.svg",
    "figures/polynomial_validation_curve_9.svg",
    "figures/randomized_search_results.csv",
    "figures/randomized_search_results_logistic_regression.csv",
    "figures/ridge_0_withreg.svg",
    "figures/ridge_alpha_0.svg",
    "figures/ridge_alpha_50.0.svg",
    "figures/ridge_alpha_50.svg",
    "figures/ridge_alpha_500.0.svg",
    "figures/ridge_alpha_500.svg",
    "figures/ridge_alpha_None.svg",
    "figures/scikit-learn-logo.svg",
    "figures/shufflesplit_1.svg",
    "figures/shufflesplit_2.svg",
    "figures/shufflesplit_3.svg",
    "figures/shufflesplit_diagram.png",
    "figures/simple_decision_tree_adult_census.png",
    "figures/splines_cubic.svg",
    "figures/splines_cubic_test.svg",
    "figures/splines_test.svg",
    "figures/style_figs.py",
    "figures/supervised.png",
    "figures/target_bias.svg",
    "figures/target_bias_0.svg",
    "figures/target_bias_1.svg",
    "figures/target_bias_2.svg",
    "figures/target_variance.svg",
    "figures/target_variance_0.svg",
    "figures/target_variance_1.svg",
    "figures/target_variance_2.svg",
    "figures/test_scores_h_shuffle=false.svg",
    "figures/tree2D_1split.svg",
    "figures/tree2D_2split.svg",
    "figures/tree2D_3split.svg",
    "figures/tree_blue_orange1.svg",
    "figures/tree_blue_orange2.svg",
    "figures/tree_blue_orange3.svg",
    "figures/tree_example.svg",
    "figures/tree_regression1.svg",
    "figures/tree_regression2.svg",
    "figures/tree_regression3.svg",
    "figures/tree_regression4.svg",
    "figures/tree_regression_structure1.svg",
    "figures/tree_regression_structure2.svg",
    "figures/tree_regression_structure3.svg",
    "figures/unsupervised.png",
    "figures/workflow.png"
  ]
}
	{
	"baseUrl": "https://github.com/INRIA/scikit-learn-mooc/raw/main/",
	"notebooks": [
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	"notebooks/01_tabular_data_exploration_ex_01.ipynb",
	"notebooks/02_numerical_pipeline_cross_validation.ipynb",
	"notebooks/02_numerical_pipeline_ex_00.ipynb",
	"notebooks/02_numerical_pipeline_ex_01.ipynb",
	"notebooks/02_numerical_pipeline_hands_on.ipynb",
	"notebooks/02_numerical_pipeline_introduction.ipynb",
	"notebooks/02_numerical_pipeline_scaling.ipynb",
	"notebooks/03_categorical_pipeline.ipynb",
	"notebooks/03_categorical_pipeline_column_transformer.ipynb",
	"notebooks/03_categorical_pipeline_ex_01.ipynb",
	"notebooks/03_categorical_pipeline_ex_02.ipynb",
	"notebooks/cross_validation_baseline.ipynb",
	"notebooks/cross_validation_ex_01.ipynb",
	"notebooks/cross_validation_ex_02.ipynb",
	"notebooks/cross_validation_ex_03.ipynb",
	"notebooks/cross_validation_ex_04.ipynb",
	"notebooks/cross_validation_ex_05.ipynb",
	"notebooks/cross_validation_grouping.ipynb",
	"notebooks/cross_validation_learning_curve.ipynb",
	"notebooks/cross_validation_nested.ipynb",
	"notebooks/cross_validation_stratification.ipynb",
	"notebooks/cross_validation_time.ipynb",
	"notebooks/cross_validation_train_test.ipynb",
	"notebooks/cross_validation_validation_curve.ipynb",
	"notebooks/datasets_ames_housing.ipynb",
	"notebooks/datasets_bike_rides.ipynb",
	"notebooks/datasets_blood_transfusion.ipynb",
	"notebooks/datasets_california_housing.ipynb",
	"notebooks/dev_features_importance.ipynb",
	"notebooks/ensemble_adaboost.ipynb",
	"notebooks/ensemble_bagging.ipynb",
	"notebooks/ensemble_ex_01.ipynb",
	"notebooks/ensemble_ex_02.ipynb",
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	"notebooks/ensemble_hist_gradient_boosting.ipynb",
	"notebooks/ensemble_hyperparameters.ipynb",
	"notebooks/ensemble_introduction.ipynb",
	"notebooks/ensemble_random_forest.ipynb",
	"notebooks/feature_selection_ex_01.ipynb",
	"notebooks/feature_selection_introduction.ipynb",
	"notebooks/feature_selection_limitation_model.ipynb",
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	"notebooks/helpers/plotting.py",
	"notebooks/linear_models_ex_01.ipynb",
	"notebooks/linear_models_ex_02.ipynb",
	"notebooks/linear_models_ex_03.ipynb",
	"notebooks/linear_models_ex_04.ipynb",
	"notebooks/linear_models_ex_05.ipynb",
	"notebooks/linear_models_regularization.ipynb",
	"notebooks/linear_regression_in_sklearn.ipynb",
	"notebooks/linear_regression_non_linear_link.ipynb",
	"notebooks/linear_regression_without_sklearn.ipynb",
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	"notebooks/matplotlibrc",
	"notebooks/metrics_classification.ipynb",
	"notebooks/metrics_ex_01.ipynb",
	"notebooks/metrics_ex_02.ipynb",
	"notebooks/metrics_regression.ipynb",
	"notebooks/parameter_tuning_ex_02.ipynb",
	"notebooks/parameter_tuning_ex_03.ipynb",
	"notebooks/parameter_tuning_grid_search.ipynb",
	"notebooks/parameter_tuning_manual.ipynb",
	"notebooks/parameter_tuning_nested.ipynb",
	"notebooks/parameter_tuning_parallel_plot.ipynb",
	"notebooks/parameter_tuning_randomized_search.ipynb",
	"notebooks/trees_classification.ipynb",
	"notebooks/trees_dataset.ipynb",
	"notebooks/trees_ex_01.ipynb",
	"notebooks/trees_ex_02.ipynb",
	"notebooks/trees_hyperparameters.ipynb",
	"notebooks/trees_regression.ipynb",
	"notebooks/video_pipeline.ipynb"
	],
	"datasets": [
	"datasets/README.md",
	"datasets/adult-census-numeric-all.csv",
	"datasets/adult-census-numeric-test.csv",
	"datasets/adult-census-numeric.csv",
	"datasets/adult-census.csv",
	"datasets/ames_housing_no_missing.csv",
	"datasets/bike_rides.csv",
	"datasets/blood_transfusion.csv",
	"datasets/cps_85_wages.csv",
	"datasets/financial-data",
	"datasets/house_prices.csv",
	"datasets/penguins.csv",
	"datasets/penguins_classification.csv",
	"datasets/penguins_regression.csv"
	],
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	"evaluation/linear_models_questions.ipynb",
	"evaluation/trees_questions.ipynb",
	"evaluation/predictive_modeling_pipeline_questions.ipynb",
	"evaluation/overfit_questions.ipynb",
	"evaluation/tuning_questions.ipynb",
	"evaluation/evaluation_questions.ipynb",
	"evaluation/sandbox_notebook.ipynb"
	],
	"figures": [
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	"figures/api_diagram-columntransformer.svg",
	"figures/api_diagram-pipeline.fit.svg",
	"figures/api_diagram-pipeline.predict.svg",
	"figures/api_diagram-predictor.fit.svg",
	"figures/api_diagram-predictor.predict.svg",
	"figures/api_diagram-predictor.score.svg",
	"figures/api_diagram-transformer.fit.svg",
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	"figures/api_diagram-transformer.transform.svg",
	"figures/api_diagram.drawio",
	"figures/bagging.svg",
	"figures/bagging0.svg",
	"figures/bagging0_cross.svg",
	"figures/bagging_cross.svg",
	"figures/bagging_fit.svg",
	"figures/bagging_line.svg",
	"figures/bagging_overfit.svg",
	"figures/bagging_reg_blue.svg",
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	"figures/boosting_trees2.svg",
	"figures/boosting_trees3.svg",
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	"figures/cross-val1-shuffle.svg",
	"figures/cross-val1.svg",
	"figures/cross-val2-shuffle.svg",
	"figures/cross-val2.svg",
	"figures/cross-val3-shuffle.svg",
	"figures/cross-val3.svg",
	"figures/cross-val4-shuffle.svg",
	"figures/cross-val4.svg",
	"figures/cross-val5-shuffle.svg",
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	"figures/cross_validation_train_test_diagram.png",
	"figures/different_models_complex_16.svg",
	"figures/different_models_complex_4.svg",
	"figures/dt_fit.svg",
	"figures/dt_overfit.svg",
	"figures/dt_underfit.svg",
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