Machine Learning for OpenCV 4 - Second Edition

Choosing the right regression metric

Evaluation for regression can be done in similar detail as we did for classification. In Chapter 3, First Steps in Supervised Learning, we also talked about some fundamental metrics for regression:

• Mean squared error: The most commonly used error metric for regression problems is to measure the squared error between the predicted and true target value for every data point in the training set, averaged across all data points (sklearn.metrics.mean_squared_error).
• Explained variance: A more sophisticated metric is to measure to what degree a model can explain the variation or dispersion of the test data (sklearn.metrics.explained_variance_score). Often, the amount of explained variance is measured using ...