Python Tutorial: Decision-Tree for Regression

Want to learn more? Take the full course at https://learn.datacamp.com/courses/machine-learning-with-tree-based-models-in-python at your own pace. More than a video, you'll learn hands-on coding & quickly apply skills to your daily work. --- Welcome back! In this video, you'll learn how to train a decision tree for a regression problem. Recall that in regression, the target variable is continuous. In other words, the output of your model is a real value. Let's motivate our discussion of regression by introducing the automobile miles-per-gallon dataset from the UCI Machine Learning Repository. This dataset consists of 6 features corresponding to the characteristics of a car and a continuous target variable labeled mpg which stands for miles-per-gallon. Our task is to predict the mpg consumption of a car given these six features. To simplify the problem, here the analysis is restricted to only one feature corresponding to the displacement of a car. This feature is denoted by displ. A 2D scatter plot of mpg versus displ shows that the mpg-consumption decreases nonlinearly with displacement. Note that linear models such as linear regression would not be able to capture such a non-linear trend. Let's see how you can train a decision tree with scikit-learn to solve this regression problem. Note that the features X and the labels y are already loaded in the environment. First, import DecisionTreeRegressor from sklearn-dot-tree and the functions train_test_split() from sklearn-dot-model_selection and mean_squared_error as MSE() from sklearn-dot-metrics. Then, split the data into 80%-train and 20%-test using train_test_split. You can now instantiate the DecisionTreeRegressor() with a maximum depth of 4 by setting the parameter max_depth to 4. In addition, set the parameter min_sample_leaf to 0-dot-1 to impose a stopping condition in which each leaf has to contain at least 10% of the training data. Now fit dt to the training set and predict the test set labels.