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One way to do that would be to fiddle with the hyperparameters manually, until you find a great combination of hyperparameter values.
All you need to do is tell it which hyperparameters you want it to experiment with, and what values to try out, and it will evaluate all the possible combinations of hyperparameter values, using cross-validation.
For example, the following code searches for the best combi‐ nation of hyperparameter values for the RandomForestRegressor:
This param_grid tells Scikit-Learn to first evaluate all 3 × 4 = 12 combinations of n_estimators and max_features hyperparameter values specified in the first dict (don't worry about what these hyperparameters mean for now; they will be explained in Chapter 7), then try all 2 × 3 = 6 combinations of hyperparameter values in the second dict, but this time with the bootstrap hyperparameter set to False instead of True (which is the default value for this hyperparameter).
All in all, the grid search will explore 12 + 6 = 18 combinations of RandomForestRe gressor hyperparameter values, and it will train each model five times (since we are using five-fold cross validation).
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