Seen vs. unseen data

Model's tend to have higher accuracy on observations they have seen before. In the candy dataset, predicting the popularity of Skittles will likely have higher accuracy than predicting the popularity of Andes Mints; Skittles is in the dataset, and Andes Mints is not.

You've built a model based on 50 candies using the dataset X_train and need to report how accurate the model is at predicting the popularity of the 50 candies the model was built on, and the 35 candies (X_test) it has never seen. You will use the mean absolute error, mae(), as the accuracy metric.

This exercise is part of the course

Model Validation in Python

Exercise instructions

Using X_train and X_test as input data, create arrays of predictions using model.predict().
Calculate model accuracy on both data the model has seen and data the model has not seen before.
Use the print statements to print the seen and unseen data.

Hands-on interactive exercise

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

# The model is fit using X_train and y_train
model.fit(X_train, y_train)

# Create vectors of predictions
train_predictions = model.predict(____)
test_predictions = model.predict(____)

# Train/Test Errors
train_error = mae(y_true=y_train, y_pred=____)
test_error = mae(y_true=y_test, y_pred=____)

# Print the accuracy for seen and unseen data
print("Model error on seen data: {0:.2f}.".format(____))
print("Model error on unseen data: {0:.2f}.".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

Current Exercise

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 Exercise 10: Course completed!