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End to End Project - Bikes Assessment - Basic - Fine-Tuning the Selected Model - Choosing set of hyperparameter combinations for Grid Search

Now, since we have "Trained" the models and selected the best model based on the performance measure(RMSE) values, let us apply Grid Search on this selected model to fine-tune the model (i.e. find the best hyperparameters for this model).

As first step for the same, let us import GridSearchCV and choose the set of hyperparameter combinations in the form of a 'parameter grid', which we will use to apply the Grid Search.

INSTRUCTIONS

Please follow the below steps:

  • Import GridSearchCV class of Scikit Learn.

  • Define a variable param_grid and set following key-value pairs in this param_grid - 'n_estimators': [120, 150], 'max_features': [10, 12], 'max_depth': [15, 28]

    param_grid = [
        {'n_estimators': [120, 150], <<your code comes here>>},
    ]
    

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