In machine learning tasks, we often need to evaluate the performance of our models using different metrics or score functions. While scikit-learn provides many built-in score functions for various tasks like classification or regression, there may be cases where we need to define a custom score function that requires additional values from our dataset other than the feature vectors X and target variable y.
In this tutorial, we will walk through the process of creating a custom score function in scikit-learn that takes additional values from our dataset. We will use the KNeighborsClassifier from scikit-learn as an example, but the same concepts can be applied to other models as well.
Step 1: Import the necessary libraries
First, let’s import the necessary libraries and load a sample dataset to work with. In this example, we will use the iris dataset included in scikit-learn.
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<title>Custom Score Function Tutorial</title>
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<h1>Custom Score Function Tutorial</h1>
<p>Import the necessary libraries</p>
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<script src="https://unpkg.com/axios/dist/axios.min.js"></script>
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</html>Step 2: Define the custom score function
Next, let’s define our custom score function that takes the additional dataset values as arguments. In this example, we will create a score function that computes the accuracy of a KNeighborsClassifier model using a specific threshold for the target variable.
<p>Define the custom score function</p>
<pre><code><script>
// Define custom score function
function custom_score(y_true, y_pred, threshold) {
// Apply threshold to y_pred
let y_pred_thresholded = y_pred >= threshold ? 1 : 0;
// Compute accuracy
let accuracy = y_true.map((true_label, i) => true_label === y_pred_thresholded[i] ? 1 : 0).reduce((a, b) => a + b, 0) / y_true.length;
return accuracy;
}
</code></pre>Step 3: Create a custom scorer using make_scorer
Now that we have defined our custom score function, we can use the make_scorer function from scikit-learn to create a custom scorer object that can be used with the cross_val_score function for model evaluation.
<p>Create a custom scorer using make_scorer</p>
<pre><code><script>
// Import necessary functions from scikit-learn
const { make_scorer } = require('scikit-learn');
// Create custom scorer using make_scorer
const custom_scorer = make_scorer(custom_score, greater_is_better=true, threshold=0.5);
</code></pre>Step 4: Using the custom scorer with cross_val_score
Finally, we can use our custom scorer object with the cross_val_score function to evaluate the performance of our model using the custom score function.
<p>Using the custom scorer with cross_val_score</p>
<pre><code><script>
// Import necessary functions from scikit-learn
const { cross_val_score, KNeighborsClassifier } = require('scikit-learn');
// Create KNeighborsClassifier model
const model = new KNeighborsClassifier();
// Evaluate model using custom scorer
const scores = cross_val_score(model, X, y, scoring=custom_scorer, cv=5);
</code></pre>And that’s it! You have successfully created a custom score function in scikit-learn that takes additional values from your dataset and used it to evaluate the performance of your model. Experiment with different custom score functions and see how they impact the evaluation of your models. Happy coding!
