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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)
Is this a unique feature?
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.
Screenshots
No response
Do you want to work on this issue?
Yes
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)