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Build Grid Search functions

In data science it is a great idea to try building algorithms, models and processes 'from scratch' so you can really understand what is happening at a deeper level. Of course there are great packages and libraries for this work (and we will get to that very soon!) but building from scratch will give you a great edge in your data science work.

In this exercise, you will create a function to take in 2 hyperparameters, build models and return results. You will use this function in a future exercise.

You will have available the X_train, X_test, y_train and y_test datasets available.

This exercise is part of the course

Hyperparameter Tuning in Python

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Exercise instructions

  • Build a function that takes two parameters called learning_rate and max_depth for the learning rate and maximum depth.
  • Add capability in the function to build a GBM model and fit it to the data with the input hyperparameters.
  • Have the function return the results of that model and the chosen hyperparameters (learning_rate and max_depth).

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Create the function
def gbm_grid_search(____, ____):

	# Create the model
    model = GradientBoostingClassifier(____=___, ____=____)
    
    # Use the model to make predictions
    predictions = model.fit(____, ____).predict(____)
    
    # Return the hyperparameters and score
    return([____, ____, accuracy_score(____, ____)])
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