How to Tell scikit-learn for Which Label the F1-Score/Precision/Recall Score is Given (in Binary Classification)?
In binary classification with scikit-learn, understanding which label (0 or 1) the F1-score, precision, and recall are calculated for is crucial. This article will guide you on how to control this behavior and interpret the results effectively.
1. Default Behavior
By default, scikit-learn’s classification metrics like F1-score, precision, and recall are calculated assuming that the **positive class is represented by label 1**, and the **negative class is represented by label 0**. If you don’t specify anything, the metrics are computed based on this implicit assumption.
2. Understanding the Concepts
- F1-Score: The harmonic mean of precision and recall, providing a balance between them.
- Precision: The proportion of correctly predicted positive instances out of all instances predicted as positive.
- Recall: The proportion of correctly predicted positive instances out of all actual positive instances.
3. Controlling the Positive Label
3.1 Specifying the ‘pos_label’ Parameter
Most scikit-learn classification metrics offer the ‘pos_label’ parameter. This allows you to explicitly specify which label should be treated as the positive class. For example, to compute the metrics for the label ‘0’:
from sklearn.metrics import f1_score, precision_score, recall_score y_true = [0, 1, 0, 0, 1] y_pred = [0, 1, 1, 0, 0] f1_0 = f1_score(y_true, y_pred, pos_label=0) precision_0 = precision_score(y_true, y_pred, pos_label=0) recall_0 = recall_score(y_true, y_pred, pos_label=0) print(f"F1-Score for label 0: {f1_0}") print(f"Precision for label 0: {precision_0}") print(f"Recall for label 0: {recall_0}")
3.2 Using ‘average’ Parameter for Micro/Macro Scores
For cases where you want overall metrics across all labels (in multi-class scenarios or binary with label swapping), use the ‘average’ parameter with options like ‘micro’, ‘macro’, or ‘weighted’.
from sklearn.metrics import f1_score # 'micro': Considers all instances and labels together f1_micro = f1_score(y_true, y_pred, average='micro') # 'macro': Calculates the average of individual label metrics f1_macro = f1_score(y_true, y_pred, average='macro') # 'weighted': Weights the individual label metrics by the number of instances in each label f1_weighted = f1_score(y_true, y_pred, average='weighted')
4. Interpretation
Remember that the default ‘pos_label=1’ assumption might not always align with the actual meaning of the labels in your specific problem. It’s crucial to understand the domain and carefully choose the appropriate ‘pos_label’ or ‘average’ setting to obtain the desired insights from the metrics.