In our previous post we learned a lot of things about Decision trees Classifier. In this post we will learn how to train a Decision tree regressor in sklearn.
Decision Tree Regressor –
Decision tree regression works similarly to decision tree classification, however instead of reducing Gini impurity or entropy, potential splits are by default measured on how much they reduce mean squared error (MSE).
How to train a Decision Tree Regressor in Sklearn?
Let’s read a dataset first.
import pandas as pd
import numpy as np
from sklearn import datasets
housing = datasets.fetch_california_housing()
X = pd.DataFrame(housing.data, columns=housing.feature_names)
y = housing.target
X.head()
Now split the data into a training and test set.
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)Next, we will train a Decision tree regressor in scikit-learn and measure the error.
from sklearn.tree import DecisionTreeRegressor
from sklearn.metrics import mean_squared_error
# create a decision tree regressor model
tree_reg = DecisionTreeRegressor(random_state=42)
# train it on the training data
tree_reg.fit(X_train, y_train)
# make predictions on the test set
y_pred = tree_reg.predict(X_test)
# measure error
mse = mean_squared_error(y_test, y_pred)
rmse = np.sqrt(mse)
rmse# output
0.7056736565432687