Simple ensemble method using random forest and AdaBoost

[SaurabhIndi]

Is this a unique feature?

• I have checked "open" AND "closed" issues and this is not a duplicate

Is your feature request related to a problem/unavailable functionality? Please describe.

This code demonstrates a simple ensemble method using random forest and AdaBoost. It combines the predictions of these two models by taking their average. You can experiment with different ensemble techniques like stacking or voting, and adjust the hyperparameters of the individual models to find the best combination for your specific problem.

Proposed Solution

Ensemble Methods: Consider combining multiple models (e.g., random forest, AdaBoost) to potentially improve performance and reduce overfitting.

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If "yes" to above, please explain how you would technically implement this (issue will not be assigned if this is skipped)

from sklearn.ensemble import RandomForestRegressor, AdaBoostRegressor
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error

Assuming you have your features (X) and target variable (y)

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

Create individual models

rf_model = RandomForestRegressor(n_estimators=100, random_state=42)
adaboost_model = AdaBoostRegressor(n_estimators=100, random_state=42)

Train the models

rf_model.fit(X_train, y_train)
adaboost_model.fit(X_train, y_train)

Make predictions

rf_predictions = rf_model.predict(X_test)
adaboost_predictions = adaboost_model.predict(X_test)

Combine predictions (simple averaging)

ensemble_predictions = (rf_predictions + adaboost_predictions) / 2

Evaluate the ensemble model

ensemble_mse = mean_squared_error(y_test, ensemble_predictions)
print("Ensemble MSE:", ensemble_mse)

[github-actions]

Ensure the issue is not similar or previously being worked on.Thanks for your time

[ModZ777]

I'd like to work on this project. please assign it to me

[saumyacoder1709]

I would like to contribute in this project by increasing accuracy of the predictions using various different models

[Niraj1608]

@SaurabhIndi assign this to me

[github-actions]

✅ This issue has been successfully closed. Thank you for your contribution and helping us improve the project! If you have any more ideas or run into other issues, feel free to open a new one. Happy coding! 🚀