Evaluating Regression Models: Metrics and Loss Functions

Introduction

To effectively engage with this resource, start by understanding its structure. Focus on evaluation metrics, model comparisons, and data preprocessing. Pay special attention to key metrics like MAE, RMSE, and R², noting their differences and applications. Visualizations, such as regression plots, provide deeper insights into model performance, so use them to reinforce concepts. As you progress, reflect on practical applications. For example, why does one model perform better than another, and how do preprocessing steps like outlier removal impact results? If you're comfortable with coding, experiment with the provided code snippets to see how changes affect performance. Which metric best suits your dataset? Applying these concepts to your own projects will transform theoretical knowledge into practical expertise.

Performance metrics are vital for supervised machine learning models. To be sure that your model is doing well in its predictions, you need to evaluate the model. Our goal is to identify how well the model performs on new data.

There are some evaluation metrics that can help you determine whether the model's predictions are accurate to a certain level of performance.

Source: Marcin Rutecki, https://www.kaggle.com/code/marcinrutecki/regression-models-evaluation-metrics/notebook#2.-Regression-Evaluation-Metrics
Licensed under the Apache License, Version 2.0 (the "License").