Score Script

The score script is a tool designed for evaluating the performance of machine learning models. It provides a simple and flexible interface for calculating various types of model evaluation metrics, such as accuracy, precision, recall, F1-score, and more.

Installation

To install the score script, simply run the following command:

pip install score

Usage

The score script can be used from the command line or imported as a module in your Python code.

Command line interface

To run the score script from the command line, simply type score followed by the desired arguments.

Here's an example command to calculate the accuracy of a classification model:

score --true-labels data/true_labels.csv --predicted-labels data/predictions.csv --metric accuracy

This command assumes that you have two CSV files, true_labels.csv and predictions.csv, that contain the true class labels and the predicted class labels, respectively.

Module usage

To use the score script in your Python code, you can import the score module and call its functions for calculating evaluation metrics.

Here's an example code snippet that calculates the F1-score of a binary classification model:

import score

true_labels = [0, 1, 0, 1, 1, 0]
predicted_labels = [1, 1, 0, 1, 0, 0]

f1_score = score.f1_score(true_labels, predicted_labels)
print(f1_score)

This code assumes that you have two lists, true_labels and predicted_labels, that contain the true class labels and the predicted class labels, respectively.

Supported metrics

The score script supports the following evaluation metrics:

• Accuracy
• Precision
• Recall
• F1-score
• ROC-AUC
• Average precision
• Log-loss

Conclusion

The score script is a powerful tool for evaluating the performance of machine learning models. Whether you're working on classification, regression, or any other type of ML problem, the score script can help you calculate the metrics you need to measure your model's success. Try it out for yourself and see how it can improve your ML workflows!