Model Evaluation

Model evaluation is a critical step in the machine learning process. It involves assessing the performance of a model on a test dataset to understand its accuracy, precision, recall, and F1 score.

Key Metrics

• Accuracy: The ratio of correctly predicted observations to the total observations.
• Precision: The ratio of correctly predicted positive observations to the total predicted positive observations.
• Recall: The ratio of correctly predicted positive observations to the all observations in actual class.
• F1 Score: The weighted average of Precision and Recall.

Tools for Model Evaluation

• Scikit-Learn: A Python-based library that provides various metrics for model evaluation.
• TensorFlow: An open-source library for machine intelligence that includes evaluation metrics.
• PyTorch: An open-source machine learning library based on the Torch library, used for evaluating models.

Machine Learning Evaluation Metrics

Further Reading

For more detailed information on model evaluation, check out our comprehensive guide on Machine Learning Metrics.