Python Feature Engineering Cookbook

How it works...

In this recipe, we combined the information of three variables from the Boston House Prices dataset into a new variable utilizing a decision tree. We loaded the dataset from scikit-learn and then separated the data into train and test sets using the train_test_split() function. Next, we created a dictionary with the decision tree parameter to optimize as keys, and a list of the values to examine as values.

Next, we created an instance of a decision tree for regression using DecisionTreeRegressor() from scikit-learn inside GridSearch(), indicating the fold cross-validation, the metric to optimize, and the dictionary with the parameters and values to examine. Next, we fit the decision tree to the three variables of interest, ...