mse_measure module¶

The mean squared error to measure the quality of a regression algorithm.

The mse_measure module implements the concept of mean squared error measures for machine learning algorithms.

This concept is implemented through the MSEMeasure class and overloads the MLErrorMeasure._compute_measure() method.

The mean squared error (MSE) is defined by

\[\begin{split}\\operatorname{MSE}(\\hat{y})=\\frac{1}{n}\\sum_{i=1}^n(\\hat{y}_i-y_i)^2,\end{split}\]

where \(\\hat{y}\) are the predictions and \(y\) are the data points.

class gemseo.mlearning.quality_measures.mse_measure.MSEMeasure(algo, fit_transformers=True)[source]

The Mean Squared Error measure for machine learning.

Parameters:

algo (MLRegressionAlgo) – A machine learning algorithm for regression.
fit_transformers (bool) –
Whether to re-fit the transformers when using resampling techniques. If False, use the transformers of the algorithm fitted from the whole learning dataset.

By default it is set to True.

algo: MLSupervisedAlgo: The machine learning algorithm whose quality we want to measure.

Examples using MSEMeasure¶