Challenge: Solving Task Using Bagging Regressor

Task

Swipe to start coding

The load_diabetes dataset contains ten baseline variables (age, sex, BMI, average blood pressure, and six blood serum measurements) for 442 diabetes patients. The target variable is a quantitative measure of disease progression one year after baseline. This dataset is used for predicting the continuous variable, representing diabetes progression, based on the given features.

Your task is to use Bagging Regressor to solve the regression problem on load_diabetes dataset:

Use a simple LinearRegression model as the base model of the ensemble.
Use the BaggingRegressor class to create an ensemble.
Use Mean Squared Error(MSE) to evaluate the results.

Solution

import numpy as np

from sklearn.datasets import load_diabetes

from sklearn.model_selection import train_test_split

from sklearn.ensemble import BaggingRegressor

from sklearn.linear_model import LinearRegression

from sklearn.metrics import mean_squared_error

# Load the Diabetes dataset

data = load_diabetes()

X, y = data.data, 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)

# Create a base model (Linear Regression)

base_model = LinearRegression()

# Create the Bagging Regressor

bagging_model = BaggingRegressor(base_model, n_estimators=10)

# Train the Bagging Regressor

bagging_model.fit(X_train, y_train)

# Make predictions on the test data

predictions = bagging_model.predict(X_test)

# Calculate mean squared error (MSE)

mse = mean_squared_error(y_test, predictions)

print(f'MSE: {mse:.4f}')

Everything was clear?

Thanks for your feedback!

Section 2. Chapter 4