gemseo.mlearning.regression.quality.mse_measure module#

The mean squared error to assess the quality of a regressor.

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 MSEMeasure(algo, fit_transformers=True)[source]#

The mean squared error to assess the quality of a regressor.

Parameters:

algo (BaseRegressor) -- 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 training dataset.

By default it is set to True.