module `ml.logreg`#

Short summary#

module mlstatpy.ml.logreg

Helpers on logistic regression.

source on GitHub

Functions#

function	truncated documentation
`criteria`	Computes Gini, information gain, likelihood on a dataset with two features assuming the first coordinates is used to …
`criteria2`	Computes Gini, information gain, likelihood on a dataset with two features assuming the first coordinates is used to …
`likelihood`	Computes $\sum_i y_i f(\theta (x_i - x_0)) + (1 - y_i) (1 - f(\theta (x_i - x_0)))$ where $f(x_i)$ is $\frac{1}{1 + e^{-x}}$ . …
`logistic`	Computes $\frac{1}{1 + e^{-x}}$ .
`plog2`	Computes $x \ln_2 x$ .
`plot_ds`	Plots a dataset, X is a dataset with two features, y contains the binary labels.
`random_set_1d`	Builds a random dataset as describes in example Arbre d’indécision.

Documentation#

Helpers on logistic regression.

source on GitHub

mlstatpy.ml.logreg.criteria(X, y)#

Computes Gini, information gain, likelihood on a dataset with two features assuming the first coordinates is used to classify.

Paramètres:

X – 2D matrix
y – binary labels

Renvoie:

dataframe

source on GitHub

mlstatpy.ml.logreg.criteria2(X, y)#

Computes Gini, information gain, likelihood on a dataset with two features assuming the first coordinates is used to classify.

Paramètres:

X – 2D matrix
y – binary labels

Renvoie:

dataframe

source on GitHub

mlstatpy.ml.logreg.likelihood(x, y, theta=1.0, th=0.0)#

Computes $\sum_i y_i f(\theta (x_i - x_0)) + (1 - y_i) (1 - f(\theta (x_i - x_0)))$ where $f(x_i)$ is $\frac{1}{1 + e^{-x}}$ .

source on GitHub

mlstatpy.ml.logreg.logistic(x)#

Computes $\frac{1}{1 + e^{-x}}$ .

source on GitHub

mlstatpy.ml.logreg.plog2(p)#

Computes $x \ln_2 x$ .

source on GitHub

mlstatpy.ml.logreg.plot_ds(X, y, ax=None, title=None)#

Plots a dataset, X is a dataset with two features, y contains the binary labels.

source on GitHub

mlstatpy.ml.logreg.random_set_1d(n, kind)#

Builds a random dataset as describes in example Arbre d’indécision.

Paramètres:

n – number of observations
kind – 2, 3, 4 (see example)

Renvoie:

array 2D

source on GitHub