From 28739a297609c01d947111d4d22966f22d69fa9c Mon Sep 17 00:00:00 2001 From: miguel Date: Wed, 6 Dec 2023 16:58:26 +0000 Subject: [PATCH] docs(model-section)remove unnecessary code and update readme --- subjects/ai/model-selection/README.md | 95 ++++++++++++++------------- 1 file changed, 50 insertions(+), 45 deletions(-) diff --git a/subjects/ai/model-selection/README.md b/subjects/ai/model-selection/README.md index 546f9edcd..ca6702db5 100644 --- a/subjects/ai/model-selection/README.md +++ b/subjects/ai/model-selection/README.md @@ -16,6 +16,7 @@ We will answer these questions today ! The topics we will cover are the one of t - Exercise 4: Validation curve and Learning curve ### Virtual Environment + - Python 3.x - NumPy - Pandas @@ -23,7 +24,7 @@ We will answer these questions today ! The topics we will cover are the one of t - Scikit-learn - Matplotlib -*Version of Pandas I used to do the exercises: 1.0.1*. +_Version of Pandas I used to do the exercises: 1.0.1_. I suggest to use the most recent one. ### **Resources** @@ -31,31 +32,35 @@ I suggest to use the most recent one. **Must read before to start the exercises** ### Biais-Variance trade off, aka Underfitting/Overfitting: - - https://machinelearningmastery.com/gentle-introduction-to-the-bias-variance-trade-off-in-machine-learning/ - - https://jakevdp.github.io/PythonDataScienceHandbook/05.03-hyperparameters-and-model-validation.html +- https://machinelearningmastery.com/gentle-introduction-to-the-bias-variance-trade-off-in-machine-learning/ + +- https://jakevdp.github.io/PythonDataScienceHandbook/05.03-hyperparameters-and-model-validation.html ### Cross-validation - - https://algotrading101.com/learn/train-test-split/ + +- https://algotrading101.com/learn/train-test-split/ --- + --- # Exercise 0: Environment and libraries The goal of this exercise is to set up the Python work environment with the required libraries. -**Note:** For each quest, your first exercice will be to set up the virtual environment with the required libraries. +**Note:** For each quest, your first exercise will be to set up the virtual environment with the required libraries. I recommend to use: - the **last stable versions** of Python. -- the virtual environment you're the most confortable with. `virtualenv` and `conda` are the most used in Data Science. -- one of the most recents versions of the libraries required +- the virtual environment you're the most comfortable with. `virtualenv` and `conda` are the most used in Data Science. +- one of the most recent versions of the libraries required 1. Create a virtual environment named `ex00`, with a version of Python >= `3.8`, with the following libraries: `pandas`, `numpy`, `jupyter`, `matplotlib` and `scikit-learn`. --- + --- # Exercise 1: K-Fold @@ -69,24 +74,25 @@ y = np.array(np.arange(1,11)) 1. Using `KFold`, perform a 5-fold cross validation. For each fold, print the train index and test index. The expected output is: - ```console - Fold: 1 - TRAIN: [2 3 4 5 6 7 8 9] TEST: [0 1] + ```console + Fold: 1 + TRAIN: [2 3 4 5 6 7 8 9] TEST: [0 1] - Fold: 2 - TRAIN: [0 1 4 5 6 7 8 9] TEST: [2 3] + Fold: 2 + TRAIN: [0 1 4 5 6 7 8 9] TEST: [2 3] - Fold: 3 - TRAIN: [0 1 2 3 6 7 8 9] TEST: [4 5] + Fold: 3 + TRAIN: [0 1 2 3 6 7 8 9] TEST: [4 5] - Fold: 4 - TRAIN: [0 1 2 3 4 5 8 9] TEST: [6 7] + Fold: 4 + TRAIN: [0 1 2 3 4 5 8 9] TEST: [6 7] - Fold: 5 - TRAIN: [0 1 2 3 4 5 6 7] TEST: [8 9] - ``` + Fold: 5 + TRAIN: [0 1 2 3 4 5 6 7] TEST: [8 9] + ``` --- + --- # Exercise 2: Cross validation (k-fold) @@ -95,7 +101,7 @@ The goal of this exercise is to learn how to use cross validation. After reading Preliminary: -- Import California Housing data set and split it in a train set and a test set (10%). Fit a linear regression on the data set. *The goal is to focus on the cross validation, that is why the code to fit the Linear Regression is given.* +- Import California Housing data set and split it in a train set and a test set (10%). Fit a linear regression on the data set. _The goal is to focus on the cross validation, that is why the code to fit the Linear Regression is given._ ```python # imports @@ -135,7 +141,7 @@ Mean of scores on validation sets: Standard deviation of scores on validation sets: 0.0214983822773466 - ``` +``` **Note: It may be confusing that the key of the dictionary that returns the results on the validation sets is `test_score`. Sometimes, the validation sets are called test sets. In that case, we run the cross validation on X_train. It means that the scores are computed on sets in the initial train set. The X_test is not used for the cross-validation.** @@ -144,24 +150,21 @@ Standard deviation of scores on validation sets: - https://machinelearningmastery.com/how-to-configure-k-fold-cross-validation/ --- + --- # Exercise 3: GridsearchCV -The goal of this exercise is to learn to use GridSearchCV to run a grid search, predict on the test set and score on the test set. +The goal here is to utilize GridSearchCV for running a grid search, making predictions, and scoring on a test set. Preliminary: -- Import California Housing data set and split it in a train set and a test set (10%). Fit a linear regression on the data set. *The goal is to focus on the gridsearch, that is why the code to fit the Linear Regression is given.* +- Import California Housing dataset, split it into a train and a test set (10%), and fit a linear regression on the dataset. ```python # imports from sklearn.datasets import fetch_california_housing from sklearn.model_selection import train_test_split -from sklearn.linear_model import LinearRegression -from sklearn.preprocessing import StandardScaler -from sklearn.impute import SimpleImputer -from sklearn.pipeline import Pipeline # data housing = fetch_california_housing() @@ -172,36 +175,38 @@ X_train, X_test, y_train, y_test = train_test_split(X, test_size=0.1, shuffle=True, random_state=43) -# pipeline -pipeline = [('imputer', SimpleImputer(strategy='median')), - ('scaler', StandardScaler()), - ('lr', LinearRegression())] -pipe = Pipeline(pipeline) ``` -1. Run `GridSearchCV` on all CPUs with 5 folds, MSE as score, Random Forest as model with: +1. Run `GridSearchCV` with the following settings: + + - Using all CPUs, perform 5-fold cross-validation. + - Scoring metric: MSE (Mean Squared Error) + - Model: Random Forest -- max_depth between 1 and 20 (at least 3 values) -- n_estimators between 1 and 100 (at least 3 values) + Hyperparameters to search: -This may take few minutes to run. + - `max_depth`: range between 1 and 20 (minimum 3 values) + - `n_estimators`: range between 1 and 100 (minimum 3 values) -*Hint*: The name of the metric to put in the parameter `scoring` is `neg_mean_squared_error`. The smaller the MSE is, the better the model is. At the contrary, The greater the R2 is the better the model is. `GridSearchCV` chooses the best model by selecting the one that maximized the score on the validation sets. And, in mathematic, maximizing a function or minimizing its opposite is equivalent. More details: + This computation might take a few minutes to run. + +_Hint_: The name of the metric to put in the parameter `scoring` is `neg_mean_squared_error`. The smaller the MSE is, the better the model is. At the contrary, The greater the R2 is the better the model is. `GridSearchCV` chooses the best model by selecting the one that maximized the score on the validation sets. And, in mathematic, maximizing a function or minimizing its opposite is equivalent. More details: - https://stackoverflow.com/questions/21443865/scikit-learn-cross-validation-negative-values-with-mean-squared-error -2. Extract the best fitted estimator, print its params, print its score on the validation set and print `cv_results_`. +2. Extract the best fitted estimator, print its parameters, its score on the validation set, and display `cv_results_`. -3. Compute the score the test set. +3. Compute the score on the test set. -**WARNING: If the score used in classification is the AUC, there is one rare case where the AUC may return an error or a warning: The fold contains only one class. In that case it can't be computed, by definition.** +**WARNING: For classification tasks using AUC score, an error or warning might occur if a fold contains only one class, rendering the AUC unable to be computed due to its definition.** --- + --- # Exercise 4: Validation curve and Learning curve -The goal of this exercise is to learn to analyse the model's performance with two tools: +The goal of this exercise is to learn to analyze the model's performance with two tools: - Validation curve - Learning curve @@ -220,7 +225,7 @@ X, y = make_classification(n_samples=100000, ``` 1. Plot the validation curve, using all CPUs, with 5 folds. The goal is to focus again on max_depth between 1 and 20. -You may need to increase the window (example: between 1 and 50 ) if you notice that other values of max_depth could have returned better results. This may take few minutes. + You may need to increase the window (example: between 1 and 50 ) if you notice that other values of max_depth could have returned better results. This may take few minutes. I do not expect that you implement all the plot from scratch, you'd better leverage the code here: @@ -230,7 +235,7 @@ The plot should look like this: ![alt text][logo_ex5q1] -[logo_ex5q1]: ./w2_day5_ex5_q1.png "Validation curve " +[logo_ex5q1]: ./w2_day5_ex5_q1.png 'Validation curve ' The interpretation is that from max_depth=10, the train score keeps increasing but the test score (or validation score) reaches a plateau. It means that choosing max_depth = 20 may lead to have an over fitted model. @@ -240,7 +245,7 @@ More details: - https://chrisalbon.com/machine_learning/model_evaluation/plot_the_validation_curve/ -2. Let us assume the gridsearch returned `clf = RandomForestClassifier(max_depth=12)`. Let's check if the models under fits, over fit or fits correctly. Plot the learning curve. These two resources will help you a lot to understand how to analyse the learning curves and how to plot them: +2. Let us assume the gridsearch returned `clf = RandomForestClassifier(max_depth=12)`. Let's check if the models under fits, over fit or fits correctly. Plot the learning curve. These two resources will help you a lot to understand how to analyze the learning curves and how to plot them: - https://machinelearningmastery.com/learning-curves-for-diagnosing-machine-learning-model-performance/ @@ -250,7 +255,7 @@ More details: ![alt text][logo_ex5q2] -[logo_ex5q2]: ./w2_day5_ex5_q2.png "Learning curve " +[logo_ex5q2]: ./w2_day5_ex5_q2.png 'Learning curve ' - **Note Plot Learning Curves**: The learning curves is detailed in the first resource.