• What is a continuous variable?
• What is a categorical variable?
• Provide two of the words that are used for the possible values of a categorical variable.
• What is a "dense layer"?
• How do entity embeddings reduce memory usage and speed up neural networks?
• What kinds of datasets are entity embeddings especially useful for?
• What are the two main families of machine learning algorithms?
• Why do some categorical columns need a special ordering in their classes? How do you do this in Pandas?
• Summarize what a decision tree algorithm does.
• Why is a date different from a regular categorical or continuous variable, and how can you preprocess it to allow it to be used in a model?
• Should you pick a random validation set in the bulldozer competition? If no, what kind of validation set should you pick?
• What is pickle and what is it useful for?
• How are mse, samples, and values calculated in the decision tree drawn in this chapter?
• How do we deal with outliers, before building a decision tree?
• How do we handle categorical variables in a decision tree?
• What is bagging?
• What is the difference between max_samples and max_features when creating a random forest?
• If you increase n_estimators to a very high value, can that lead to overfitting? Why or why not?
• In the section "Creating a Random Forest", just after <>, why did preds.mean(0) give the same result as our random forest?
• What is "out-of-bag-error"?