Major classes are sir.SlicedInverseRegression and
save.SlicedAverageVarianceEstimation.
sir.SlicedInverseRegression([n_directions, …]) |
Sliced Inverse Regression (SIR) [1] |
save.SlicedAverageVarianceEstimation([…]) |
Sliced Average Variance Estimation (SAVE) [1] |
Datasets used in the examples as well as to test the algorithms are contained in the datasets module.
datasets.make_cubic([n_samples, n_features, …]) |
Generates a dataset with a cubic response curve. |
datasets.make_quadratic([n_samples, …]) |
Generates a dataset with a quadratic response curve. |
datasets.make_polynomial([n_samples, …]) |
Generates a dataset with a polynomial response curve that combines a quadratic and cubic response. |
base.grouped_sum(array, groups) |
Sums an array by groups. |
base.slice_y(y[, n_slices]) |
Determine non-overlapping slices based on the target variable, y. |