Build & evaluate the best model

Using cross-validation you were able to identify the best model for predicting life_expectancy using all the features in gapminder. Now that you've selected your model, you can use the independent set of data (testing_data) that you've held out to estimate the performance of this model on new data.

You will build this model using all training_data and evaluate using testing_data.

Diese Übung ist Teil des Kurses

Machine Learning in the Tidyverse

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Anleitung zur Übung

Use ranger() to build the best performing model (mtry = 4) using all of the training data. Assign this to best_model.
Extract the life_expectancy column from testing_data and assign it to test_actual.
Predict life_expectancy using the best_model on the testing data and assign it to test_predicted.
Calculate the MAE using test_actual and test_predicted vectors.

Interaktive Übung

Vervollständige den Beispielcode, um diese Übung erfolgreich abzuschließen.

# Build the model using all training data and the best performing parameter
best_model <- ranger(formula = ___, data = ___,
                     mtry = ___, num.trees = 100, seed = 42)

# Prepare the test_actual vector
test_actual <- testing_data$___

# Predict life_expectancy for the testing_data
test_predicted <- predict(___, ___)$predictions

# Calculate the test MAE
mae(___, ___)

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