Using decision trees for regression

Decision trees for regression are very similar to decision trees for classification. The procedure for developing a regression model consists of four parts:

• Load the dataset
• Split the set into training/testing subsets
• Instantiate a decision tree regressor and train it
• Score the model on the test subset

Getting ready

For this example, load scikit-learn's diabetes dataset:

#Use within an Jupyter notebook
%matplotlib inline   

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

from sklearn.datasets import load_diabetes

diabetes = load_diabetes()

X = diabetes.data
y = diabetes.target

X_feature_names = ['age', 'gender', 'body mass index', 'average blood pressure','bl_0','bl_1','bl_2','bl_3','bl_4','bl_5']

Now that we have loaded the dataset, we must split the data into training and testing subsets. Before doing that, however, visualize the target variable using pandas:

pd.Series(y).hist(bins=50)

This is a regression example, and we cannot use...