evaluate-model

To measure machine learning model performance, you evaluate predictions against ground truth data using specified metrics. This Skill calculates accuracy, precision, recall, MSE, and MAE to provide a standardized assessment of model effectiveness.

For classification and regression evaluation, this Skill calculates accuracy, precision, and recall for classification tasks, alongside Mean Squared Error (MSE) and Mean Absolute Error (MAE) for regression tasks to assess model accuracy.

You can detect overfitting by evaluating your model on a held-out test dataset. This Skill measures performance on unseen data, helping you identify if your model performs poorly and requires adjustments to meet project requirements.

Yes, you can compare different model architectures or training runs. By calculating standardized performance metrics like accuracy and MSE on identical test datasets, this Skill enables objective comparative analysis between models.

Yes, you need ExTensor types for predictions and ground truth data to calculate accuracy and precision. The Skill uses Mojo implementations for metric calculations, requiring these specific input formats to process the evaluation.

The best way to evaluate an image classification model is to assess its accuracy and precision on a held-out test set. This Skill measures performance using provided predictions and ground truth data to determine if it meets requirements.