Lesson 1, Section 5: Introduction to Machine Learning
Model evaluation:
After training a machine learning model, it is important to evaluate its performance to assess how well it generalizes to unseen data. Here are some key aspects of model evaluation:
A. Training set and test set:
To evaluate a model’s performance, it is common to split the available labeled data into two separate sets: a training set and a test set. The model is trained on the training set, and its performance is evaluated on the test set. The idea is to measure how well the model performs on unseen data that it hasn’t been trained on.
B. Evaluation metrics for classification:
For classification tasks, several evaluation metrics can be used to assess the performance of a model. Here are some commonly used metrics:
i. Accuracy: Accuracy measures the proportion of correctly predicted instances out of the total number of instances in the test set. It is a simple and intuitive metric, but it can be misleading if the dataset is imbalanced.
ii. Precision and recall: Precision measures the proportion of true positive predictions out of all positive predictions, while recall measures the proportion of true positive predictions out of all actual positive instances. Precision focuses on the correctness of positive predictions, while recall focuses on the coverage of positive instances.
iii. F1-score: The F1-score is the harmonic mean of precision and recall. It provides a single metric that balances both precision and recall.
iv. Confusion matrix: A confusion matrix is a table that summarizes the performance of a classification model. It shows the number of true positive, true negative, false positive, and false negative predictions.
C. Evaluation metrics for regression:
For regression tasks, different evaluation metrics are used to measure the performance of a model. Some commonly used metrics include:
i. Mean Squared Error (MSE): MSE measures the average squared difference between the predicted and actual values. It gives higher weight to larger errors.
ii. Mean Absolute Error (MAE): MAE measures the average absolute difference between the predicted and actual values. It treats all errors equally.
iii. R-squared (coefficient of determination): R-squared represents the proportion of the variance in the dependent variable (the target) that can be explained by the independent variables (the features). It ranges from 0 to 1, with 1 indicating a perfect fit.
D. Cross-validation:
Cross-validation is a technique used to estimate the performance of a model by partitioning the available data into multiple subsets or “folds.” The model is trained and evaluated on different combinations of these folds, providing a more robust estimate of its performance. Common cross-validation techniques include k-fold cross-validation and stratified k-fold cross-validation.
Understanding these evaluation metrics will help you assess the performance of your machine learning models and make informed decisions about model selection and fine-tuning.
In the next part of the lesson, we can discuss feature engineering, which plays a crucial role in improving model performance. Let me know if you’re ready to proceed or if you have any questions related to model evaluation!