Скачать или смотреть Creating a Custom Scorer in Grid Search with a Third Parameter in Python's scikit-learn

Learn how to implement a custom scoring function in a grid search while including additional input parameters by following this guide.
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How to Create a Custom Scorer with a Third Parameter in Grid Search using Python's scikit-learn

When working with machine learning models, especially during hyperparameter tuning using grid search, it’s common to want to define a custom scoring mechanism. One particularly interesting case arises when you need to incorporate additional data into your scoring function. In this guide, we will explore how to create a custom scorer that accepts a third parameter while performing a grid search using Python’s scikit-learn.

Problem Overview

Typically, when you set up a grid search, you’ve got your training and validation datasets, and you might want to take additional contextual information—the sample_value—into account when calculating the score. The challenge? You want to ensure that your custom scoring function can effectively distinguish between the training and validation datasets when they both utilize this additional parameter.

The Example Scenario

Imagine you have a random forest classifier and you want to execute a grid search over several hyperparameters while measuring performance with a custom scoring method that considers both the predicted and true labels alongside a sample_value. Here’s how you would set it up, followed by the problems you'd run into when using the default functionality.

Initial Setup

First, let’s lay the groundwork with the necessary imports and defining our classifying model:

[[See Video to Reveal this Text or Code Snippet]]

Defining the Custom Scorer

We create a function RF_metric that calculates scores based on three parameters: y_true, y_pred, and sample_value.

[[See Video to Reveal this Text or Code Snippet]]

This function creates a dataframe that allows you to calculate a cumulative score based on the daily predictions relative to the true values, adjusted by the sample_value for each instance.

Setting Up the Data

Next, we generate some sample data to test our grid search:

[[See Video to Reveal this Text or Code Snippet]]

With our data prepared, we can define our training and validation sets based on dates, ensuring each set is well-defined.

The Pitfall with make_scorer

When using make_scorer, the critique is that it assumes a certain input signature, which may inadvertently lead to errors when you attempt to include your sample_value. The error you're likely to encounter is:

[[See Video to Reveal this Text or Code Snippet]]

This issue arises as grid search processes the training data while trying to reference the validation sample values.

The Solution

The best way to overcome this limitation is by creating your own scoring callable without using make_scorer. By doing so, you allow yourself full access to all input parameters during evaluation. Here's how you can proceed:

Custom Scoring Callable

Create a custom scoring function to replace make_scorer, which adheres to the (estimator, X, y) signature:

[[See Video to Reveal this Text or Code Snippet]]

Performing Grid Search

Finally, set up your GridSearchCV as follows:

[[See Video to Reveal this Text or Code Snippet]]

This approach allows you to input all necessary arguments efficiently without running into length mismatch errors or confusion between training and validation scoring.

Conclusion

Navigating the customization of scoring functions in grid search can initially pose challenges, especially with additional parameters. By appropriately defining a custom scoring callable, you can tailor your models efficiently to suit the specifics of your dataset. This flexibility ultimately leads to better results and a more robust model.

If you have any questions or need further clarification on this topic, feel free to reach out or leave a comment! Happy coding!