Welcome to the Model Evaluation Tutorials section! Here, you will find valuable resources to understand and improve the evaluation of machine learning models.
Introduction
Model evaluation is a critical step in the machine learning process. It helps us understand how well our models are performing and where they might need improvement. In this section, we will cover various aspects of model evaluation, including:
- Performance Metrics: Different ways to measure the performance of a model.
- Cross-Validation: Techniques to ensure that our model generalizes well to unseen data.
- Error Analysis: Understanding where and why our model is making mistakes.
Performance Metrics
When evaluating a model, it's important to choose the right performance metrics. Here are some commonly used metrics:
- Accuracy: The proportion of correctly classified instances.
- Precision: The proportion of true positives among all positives predicted by the model.
- Recall: The proportion of true positives among all actual positives.
- F1 Score: The harmonic mean of precision and recall.
Cross-Validation
Cross-validation is a technique used to assess how well a model will generalize to an independent data set. Here are some common types of cross-validation:
- K-Fold Cross-Validation: Splitting the data into K subsets and using K-1 subsets for training and the remaining one for validation.
- Stratified K-Fold Cross-Validation: Ensuring that each fold is a good representative of the whole by preserving the percentage of samples for each class.
Error Analysis
Error analysis helps us understand where and why our model is making mistakes. By analyzing the errors, we can identify potential issues and improve our model.
- Confusion Matrix: A table that provides a summary of the prediction results on a set of test data for a classification problem.
- ROC Curve: A plot that illustrates the diagnostic ability of a binary classifier system as its discrimination threshold is varied.
Additional Resources
For more in-depth learning, we recommend checking out our Machine Learning Basics section.