This tutorial will guide you through a case study using Decision Tree Regression. Decision trees are a powerful tool for both classification and regression tasks. In this example, we will focus on regression.
Introduction
Decision Tree Regression is a supervised learning algorithm that uses a decision tree model to predict continuous values. It is similar to Decision Tree Classification, but instead of predicting classes, it predicts numerical values.
Data Preparation
Before we dive into the model, let's first understand the dataset we'll be using. We will use the Boston Housing dataset, which contains information about the housing values in the Boston area.
You can find more information about the dataset here.
Building the Model
To build the model, we will use the DecisionTreeRegressor class from the sklearn.tree module.
from sklearn.tree import DecisionTreeRegressor
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error
# Load the dataset
data = load_boston()
# Split the data into features and target
X = data.data
y = data.target
# Split the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# Initialize the Decision Tree Regressor
regressor = DecisionTreeRegressor()
# Train the model
regressor.fit(X_train, y_train)
# Predict on the test set
y_pred = regressor.predict(X_test)
# Calculate the mean squared error
mse = mean_squared_error(y_test, y_pred)
print(f"Mean Squared Error: {mse}")
Evaluating the Model
After training the model, we need to evaluate its performance. In this case, we used the Mean Squared Error (MSE) metric to measure the accuracy of our predictions.
Conclusion
In this tutorial, we covered the basics of Decision Tree Regression using the Boston Housing dataset. Decision trees are a versatile tool that can be used for both classification and regression tasks. For more information on decision trees, you can check out our Decision Tree Tutorial.