Machine Learning

Métriques

mlstatpy.ml.MlGridBenchMark (self, name, datasets, clog = None, fLOG = <function noLOG at 0x7f587691b310>, path_to_images = “.”, cache_file = None, progressbar = None, graphx = None, graphy = None, params)

The class tests a list of model over a list of datasets.

mlstatpy.ml.ROC (self, y_true = None, y_score = None, sample_weight = None, df = None)

Helper to draw a ROC curve.

mlstatpy.ml.voronoi_estimation_from_lr (L, B, C = None, D = None, cl = 0, qr = True, max_iter = None, verbose = False)

Determines a Voronoi diagram close to a convex partition defined by a logistic regression in n classes. M \in \mathbb{M}_{nd} a row matrix (L_1, ..., L_n). Every border between two classes i and j is defined by: \scal{L_i}{X} + B = \scal{L_j}{X} + B.

The function looks for a set of points from which the Voronoi diagram can be inferred. It is done through a linear regression with norm L1. See Régression logistique, diagramme de Voronoï, k-Means.

Tree and neural networks

mlstatpy.ml._neural_tree_node.NeuralTreeNode (self, weights, bias = None, activation = “sigmoid”, nodeid = -1, tag = None)

One node in a neural network.

mlstatpy.ml.neural_tree.NeuralTreeNet (self, dim, empty = True)

Node ensemble.

API Optimisation