Articles - Regression Model Validation

When building a regression model (Chapter @ref(linear-regression)), you need to evaluate the goodness of the model, that is how well the model fits the training data used to build the model and how accurate is the model in predicting the outcome for new unseen test observations.

In this part, you’ll learn techniques for assessing regression model accuracy and for validating the performance of the model. We’ll also provide practical examples in R.

The following chapters are covered:

• Regression Model Accuracy Metrics (Chapter @ref(regression-model-accuracy-metrics)) for measuring the performance of a regression model.
• Cross-validation (Chapter @ref(cross-validation)) and bootstrap resampling (Chapter @ref(bootstrap-resampling)) for validating the model on a test data.

The Book:

Machine Learning Essentials: Practical Guide in R