Calibrate a machine learning algorithm¶
Calibration of a machine learning algorithm¶
A machine learning algorithm depends on hyperparameters, e.g. number of clusters for a clustering algorithm, regularization constant for a regression model, kernel for a Gaussian process regression, … Its quality of generalization depends on the values of these hyperparameters. Thus, the hyperparameters minimizing the learning quality measure are rarely those minimizing the generalization one. Classically, the generalization one decreases before growing again as the model becomes more complex, while the learning error keeps decreasing. This phenomenon is called the curse of dimensionality.
In this module, the
MLAlgoCalibration class aims to calibrate the
hyperparameters in order to minimize this generalization quality measure
over a calibration parameter space. This class relies on the
MLAlgoAssessor class which is a discipline
built from a machine learning algorithm (
a dataset (
Dataset), a quality measure (
and various options for data scaling, quality measure
and machine learning algorithm. The inputs of this discipline are
hyperparameters of the machine learning algorithm while the output is
the quality criterion.