Fundamentals of Tuning Machine Learning Hyperparameters

▤ Summary

For each example we: • Use a decision forest model, similar to the one we previously built to predict the U.S. output gap. • Uses a combination of common economic indicators and GDP subcomponents as predictors of CBO-based U.S. output gap. The first step for our hyperparameter tuning example, is to load our data and split it into training and testing datasets. Now that weve set our default non-tuning parameters we will perform our grid search to tune the features per node. Stay tuned, because next time we will take a deeper dive into how to think about the data and which hyperparameter settings make sense to try out.

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