Mean absolute error

Communicating modeling results can be difficult. However, most clients understand that on average, a predictive model was off by some number. This makes explaining the mean absolute error easy. For example, when predicting the number of wins for a basketball team, if you predict 42, and they end up with 40, you can easily explain that the error was two wins.

In this exercise, you are interviewing for a new position and are provided with two arrays. y_test, the true number of wins for all 30 NBA teams in 2017 and predictions, which contains a prediction for each team. To test your understanding, you are asked to both manually calculate the MAE and use sklearn.

This exercise is part of the course

Model Validation in Python

Exercise instructions

Manually calculate the MAE using n as the number of observations predicted.
Calculate the MAE using sklearn.
Print off both accuracy values using the print statements.

Hands-on interactive exercise

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

from sklearn.metrics import mean_absolute_error

# Manually calculate the MAE
n = ____(predictions)
mae_one = sum(____(y_test - predictions)) / n
print('With a manual calculation, the error is {}'.format(____))

# Use scikit-learn to calculate the MAE
mae_two = ____(____, ____)
print('Using scikit-learn, the error is {}'.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 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

Current Exercise

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!