Assignment no. 4. 4. Hands-on Exercises with scikit-learn Library

2. Implement a Random Forest Regressor:

o Train and evaluate a Random Forest Regressor on the Boston Housing dataset.

A Python program to train and evaluate a Random Forest Regressor on the Boston Housing dataset.

The Boston Housing dataset has been deprecated in scikit-learn due to ethical concerns, but we can load it from other sources such as sklearn.datasets.fetch_openml. Here's how you can implement the model using an alternative data-fetching method.

Code to Train and Evaluate a Random Forest Regressor on the Boston Housing Dataset:

# Import necessary libraries

from sklearn.datasets import fetch_openml

from sklearn.model_selection import train_test_split

from sklearn.ensemble import RandomForestRegressor

from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score

# Load the Boston Housing dataset using fetch_openml

boston = fetch_openml(name='boston', version=1, as_frame=True)

X, y = boston.data, boston.target

# Split the data into training and testing sets (70% train, 30% test)

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

# Initialize the Random Forest Regressor

rf_regressor = RandomForestRegressor(n_estimators=100, random_state=42)

# Train the regressor

rf_regressor.fit(X_train, y_train)

# Predict the test set results

y_pred = rf_regressor.predict(X_test)

# Evaluate the performance

# 1. Mean Squared Error

mse = mean_squared_error(y_test, y_pred)

print("Mean Squared Error:", mse)

# 2. Mean Absolute Error

mae = mean_absolute_error(y_test, y_pred)

print("Mean Absolute Error:", mae)

# 3. R-squared Score

r2 = r2_score(y_test, y_pred)

print("R-squared:", r2)

Output Example: 

Mean Squared Error: 9.2658

Mean Absolute Error: 2.0345

R-squared: 0.877

Explanation:

Explanation of Metrics:

1. Mean Squared Error (MSE): Measures the average of the squares of the errors between the predicted and actual values.

2. Mean Absolute Error (MAE): Measures the average of the absolute errors between the predicted and actual values.

3. R-squared (R²): Indicates how well the model's predictions match the true data. Values closer to 1 indicate a better fit.

This program trains the Random Forest Regressor on the Boston Housing dataset and evaluates it using MSE, MAE, and R² score. The output shows the performance of the trained model on the test set.