threshopt 0.1.0

Automatic threshold optimization for binary classifiers.

threshopt

Threshold Optimization Library for Binary Classification

threshopt is a lightweight Python library designed to help find the optimal decision threshold for binary classifiers, improving model performance by customizing the threshold instead of relying on the default 0.5.

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Features

• Optimize decision thresholds based on any metric (e.g. accuracy, F1-score, G-Mean, Youden’s J)\
• Supports cross-validation threshold optimization for robust model tuning\
• Easy integration with any scikit-learn compatible model\
• Built-in common metrics and ability to use custom metrics\
• Visualize confusion matrices and prediction score distributions (optional)

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Installation

bash pip install -e .

Install in editable mode from the project root directory.

Quickstart

from threshopt import optimize_threshold, optimize_threshold_cv, gmean_score
from sklearn.ensemble import RandomForestClassifier
from sklearn.datasets import load_breast_cancer
from sklearn.metrics import f1_score

# Load data
data = load_breast_cancer()
X, y = data.data, data.target

# Train model
model = RandomForestClassifier(random_state=42)
model.fit(X, y)

# Optimize threshold on the test set
best_thresh, best_val = optimize_threshold(model, X, y, metric=f1_score)
print(f"Best threshold: {best_thresh:.2f}, F1-score: {best_val:.4f}")

# Optimize threshold with cross-validation
best_thresh_cv, best_val_cv = optimize_threshold_cv(model, X, y, metric=gmean_score, cv=5)
print(f"CV best threshold: {best_thresh_cv:.2f}, CV best metric: {best_val_cv:.4f}")


# Load data

data = load_breast_cancer() X, y = data.data, data.target

# Train model

model = RandomForestClassifier(random_state=42) model.fit(X, y)

# Optimize threshold on the test set

best_thresh, best_val = optimize_threshold(model, X, y, metric=f1_score) print(f"Best threshold: {best_thresh:.2f}, F1-score: {best_val:.4f}")

# Optimize threshold with cross-validation

best_thresh_cv, best_val_cv = optimize_threshold_cv(model, X, y, metric=gmean_score, cv=5) print(f"CV best threshold: {best_thresh_cv:.2f}, CV best metric: {best_val_cv:.4f}")

Metrics

Included metrics:

• gmean_score: Geometric Mean of sensitivity and specificity\
• youden_j_stat: Youden’s J statistic (sensitivity + specificity - 1)\
• balanced_acc_score: Balanced Accuracy (wrapper around scikit-learn)

You can also pass any metric function with signature metric(y_true, y_pred).

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Contributing

Contributions are welcome! Please open issues or submit pull requests.

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License

This project is licensed under the Apache License 2.0 - see the LICENSE file for details.