Classification predictions

In model validation, it is often important to know more about the predictions than just the final classification. When predicting who will win a game, most people are also interested in how likely it is a team will win.

Probability	Prediction	Meaning
0 < .50	0	Team Loses
.50 +	1	Team Wins

In this exercise, you look at the methods, .predict() and .predict_proba() using the tic_tac_toe dataset. The first method will give a prediction of whether Player One will win the game, and the second method will provide the probability of Player One winning. Use rfc as the random forest classification model.

This exercise is part of the course

Model Validation in Python

Exercise instructions

Create two arrays of predictions. One for the classification values and one for the predicted probabilities.
Use the .value_counts() method for a pandas Series to print the number of observations that were assigned to each class.
Print the first observation of probability_predictions to see how the probabilities are structured.

Hands-on interactive exercise

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

# Fit the rfc model. 
rfc.fit(X_train, y_train)

# Create arrays of predictions
classification_predictions = rfc.____(X_test)
probability_predictions = rfc.____(X_test)

# Print out count of binary predictions
print(pd.Series(____).____())

# Print the first value from probability_predictions
print('The first predicted probabilities are: {}'.format(____[____]))

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

Current Exercise

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 Exercise 10: Course completed!