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The Decision Functions

1. What is the Decision Function?

  • The decision_function method is provided by many classifiers in scikit-learn.
  • It returns a continuous score for each sample, representing the classifier’s confidence or margin.
  • This score reflects how strongly the model favors one class over another in binary classification, or a more complex set of scores in multiclass classification.

2. Shape and Output of decision_function

  • For binary classification, the output shape is (n_samples,).
  • Each value is a floating-point number indicating the degree to which the sample belongs to the positive class.
  • Positive values indicate a preference for the positive class; negative values indicate a preference for the negative class.
  • For multiclass classification, the output is usually a 2D array of shape (n_samples, n_classes), providing scores for each class.

3. Interpretation of decision_function Scores

  • The sign of the value (positive or negative) determines the predicted class.
  • The magnitude represents the confidence or "distance" from the decision boundary.
  • The larger the absolute value, the more confident the model is in its classification.

Example:

print("Decision function values:\n", classifier.decision_function(X_test)[:6])
# Outputs something like:
# [4.5, -1.2, 0.3, 5.0, -3.1, ...]
  • Here, values like 4.5 or 5.0 indicate strong confidence in the positive class; -1.2 or -3.1 indicate strong preference for the negative class.

4. Relationship to Prediction Threshold

  • For binary classifiers, prediction is derived by thresholding:
  • Predicted class = positive if decision_function score > 0.
  • Predicted class = negative otherwise.
  • This threshold can be adjusted:
  • Changing threshold impacts false positives/negatives.
  • Adjusting threshold can improve metrics like precision and recall in imbalanced data.

5. Examples of Classifiers Using decision_function

  • Support Vector Machines (SVMs) use decision_function to provide margin distances from the decision boundary.
  • GradientBoostingClassifier also provides decision_function for more granular confidence.
  • Logistic regression usually does not provide decision_function but provides predict_proba instead (log odds can be considered similar).

6. Advantages of decision_function Over predict_proba

  • decision_function outputs raw scores, which might be more informative for some models.
  • These raw scores can be transformed into probabilities with calibration methods like Platt scaling.
  • For models like SVMs, predict_proba is a wrapper over decision_function with a calibration step.
  • Users can set custom thresholds on decision_function to better control classification decisions.

7. Use in Model Evaluation

  • decision_function outputs enable construction of ROC curves, which plot True Positive Rate vs False Positive Rate at different thresholds.
  • By varying the decision threshold, you can evaluate model performance across thresholds.
  • Thus, decision_function is crucial for comprehensive model assessment beyond accuracy.

8. Example Code Snippet (from the book)

from sklearn.ensemble import GradientBoostingClassifier
 
# Suppose we have a trained GradientBoostingClassifier called gbrt
print("X_test.shape:", X_test.shape)
print("Decision function shape:", gbrt.decision_function(X_test).shape)
 
print("Decision function:\n", gbrt.decision_function(X_test)[:6])

Output might be:

X_test.shape: (25, 2)
Decision function shape: (25,)
Decision function:
[4. 2.5 1.3 0.7 -1.2 -3.4]

Explanation: These values show the strength of model preference for the positive class.


9. Summary Points

Aspect

                 Details

Purpose

Measures confidence or margin in classification

Output (Binary)

Array of floats (n_samples,) indicating class preference

Output (Multiclass)

Array of floats (n_samples, n_classes) with scores per class

Interpretation

Positive = positive class, Negative = negative class; magnitude = confidence

Thresholding

Default threshold at 0 to convert to class labels

Usage

Enables custom thresholds, ROC analysis, model calibration

Example models

SVM, Gradient Boosting, some ensemble classifiers

 

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