module mltricks.sklearn_example_classifier

Inheritance diagram of papierstat.mltricks.sklearn_example_classifier

Short summary

module papierstat.mltricks.sklearn_example_classifier

Defines SkCustomKnn

source on GitHub

Classes

class

truncated documentation

SkCustomKnn

Implements the k-Nearest Neighbors as an example.

Methods

method

truncated documentation

__init__

constructor

decision_function

Computes the output of the model in case of a regressor, matrix with a score for each class and each sample …

distance2weight

Converts a distance to weight.

fit

Train a k-NN model. There is not much to do except storing the training examples.

knn_search

Finds the k nearest neighbors for x.

predict

Predicts, usually, it calls the decision_function

Documentation

Defines SkCustomKnn

source on GitHub

class papierstat.mltricks.sklearn_example_classifier.SkCustomKnn(k=1)[source]

Bases : mlinsights.sklapi.sklearn_base_classifier.SkBaseClassifier

Implements the k-Nearest Neighbors as an example.

source on GitHub

constructor

Paramètres

k – number of neighbors to considers

source on GitHub

__init__(k=1)[source]

constructor

Paramètres

k – number of neighbors to considers

source on GitHub

decision_function(X)[source]

Computes the output of the model in case of a regressor, matrix with a score for each class and each sample for a classifier.

Paramètres

X – Samples, {array-like, sparse matrix}, shape = (n_samples, n_features)

Renvoie

array, shape = (n_samples,.), Returns predicted values.

source on GitHub

distance2weight(d)[source]

Converts a distance to weight.

Paramètres

d – distance

Renvoie

weight (1/(d+1))

source on GitHub

fit(X, y=None, sample_weight=None)[source]

Train a k-NN model. There is not much to do except storing the training examples.

Paramètres
  • X – Training data, numpy array or sparse matrix of shape [n_samples,n_features]

  • y – Target values, numpy array of shape [n_samples, n_targets] (optional)

  • sample_weight – Weight values, numpy array of shape [n_samples, n_targets] (optional)

Renvoie

self : returns an instance of self.

source on GitHub

Finds the k nearest neighbors for x.

Paramètres

x – vector

Renvoie

k-nearest neighbors list( (distance**2, index) )

source on GitHub

predict(X)[source]

Predicts, usually, it calls the decision_function method.

Paramètres

X – Samples, {array-like, sparse matrix}, shape = (n_samples, n_features)

Renvoie

self : returns an instance of self.

source on GitHub