Selecting the best precision model

Your boss has offered to pay for you to see three sports games this year. Of the 41 home games your favorite team plays, you want to ensure you go to three home games that they will definitely win. You build a model to decide which games your team will win.

To do this, you will build a random search algorithm and focus on model precision (to ensure your team wins). You also want to keep track of your best model and best parameters, so that you can use them again next year (if the model does well, of course). You have already decided on using the random forest classification model rfc and generated a parameter distribution param_dist.

This exercise is part of the course

Model Validation in Python

Exercise instructions

Create a precision scorer, precision using make_scorer(<scoring_function>).
Complete the random search method by using rfc and param_dist.
Use rs.cv_results_ to print the mean test scores.
Print the best overall score.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

from sklearn.metrics import precision_score, make_scorer

# Create a precision scorer
precision = ____(____)
# Finalize the random search
rs = RandomizedSearchCV(
  estimator=____, param_distributions=____,
  scoring = precision,
  cv=5, n_iter=10, random_state=1111)
rs.fit(X, y)

# print the mean test scores:
print('The accuracy for each run was: {}.'.format(rs.cv_results_['____']))
# print the best model score:
print('The best accuracy for a single model was: {}'.format(rs.____))

Edit and Run Code

This exercise is part of the course

Model Validation in Python

IntermediateSkill Level

4.8+

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Before we can validate models, we need an understanding of how to create and work with them. This chapter provides an introduction to running regression and classification models in scikit-learn. We will use this model building foundation throughout the remaining chapters.

Exercise 1: Introduction to model validation Exercise 2: Modeling steps Exercise 3: Seen vs. unseen data Exercise 4: Regression models Exercise 5: Set parameters and fit a model Exercise 6: Feature importances Exercise 7: Classification models Exercise 8: Classification predictions Exercise 9: Reusing model parameters Exercise 10: Random forest classifier

This chapter focuses on the basics of model validation. From splitting data into training, validation, and testing datasets, to creating an understanding of the bias-variance tradeoff, we build the foundation for the techniques of K-Fold and Leave-One-Out validation practiced in chapter three.

Exercise 1: Creating train, test, and validation datasets Exercise 2: Create one holdout set Exercise 3: Create two holdout sets Exercise 4: Why use holdout sets Exercise 5: Accuracy metrics: regression models Exercise 6: Mean absolute error Exercise 7: Mean squared error Exercise 8: Performance on data subsets Exercise 9: Classification metrics Exercise 10: Confusion matrices Exercise 11: Confusion matrices, again Exercise 12: Precision vs. recall Exercise 13: The bias-variance tradeoff Exercise 14: Error due to under/over-fitting Exercise 15: Am I underfitting?

Holdout sets are a great start to model validation. However, using a single train and test set if often not enough. Cross-validation is considered the gold standard when it comes to validating model performance and is almost always used when tuning model hyper-parameters. This chapter focuses on performing cross-validation to validate model performance.

Exercise 1: The problems with holdout sets Exercise 2: Two samples Exercise 3: Potential problems Exercise 4: Cross-validation Exercise 5: scikit-learn's KFold()Exercise 6: Using KFold indices Exercise 7: sklearn's cross_val_score()Exercise 8: scikit-learn's methods Exercise 9: Implement cross_val_score()Exercise 10: Leave-one-out-cross-validation (LOOCV)Exercise 11: When to use LOOCV Exercise 12: Leave-one-out-cross-validation

The first three chapters focused on model validation techniques. In chapter 4 we apply these techniques, specifically cross-validation, while learning about hyperparameter tuning. After all, model validation makes tuning possible and helps us select the overall best model.

Exercise 1: Introduction to hyperparameter tuning Exercise 2: Creating Hyperparameters Exercise 3: Running a model using ranges Exercise 4: RandomizedSearchCV Exercise 5: Preparing for RandomizedSearch Exercise 6: Implementing RandomizedSearchCV Exercise 7: Selecting your final model Exercise 8: Best classification accuracy Exercise 9: Selecting the best precision model

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