skltemplate.TemplateClassifier

class skltemplate.TemplateClassifier(demo_param='demo')

An example classifier which implements a 1-NN algorithm.

For more information regarding how to build your own classifier, read more in the User Guide.

Parameters

demo_paramstr, default=’demo’

A parameter used for demonstation of how to pass and store paramters.

Attributes

X_ndarray, shape (n_samples, n_features)

The input passed during fit().

y_ndarray, shape (n_samples,)

The labels passed during fit().

classes_ndarray, shape (n_classes,)

The classes seen at fit().

__init__(self, demo_param='demo')

Initialize self. See help(type(self)) for accurate signature.

fit(self, X, y)

A reference implementation of a fitting function for a classifier.

Parameters

Xarray-like, shape (n_samples, n_features)

The training input samples.

yarray-like, shape (n_samples,)

The target values. An array of int.

Returns

selfobject

Returns self.

get_params(self, deep=True)

Get parameters for this estimator.

Parameters

deepbool, default=True

If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns

paramsmapping of string to any

Parameter names mapped to their values.

predict(self, X)

A reference implementation of a prediction for a classifier.

Parameters

Xarray-like, shape (n_samples, n_features)

The input samples.

Returns

yndarray, shape (n_samples,)

The label for each sample is the label of the closest sample seen during fit.

score(self, X, y, sample_weight=None)

Return the mean accuracy on the given test data and labels.

In multi-label classification, this is the subset accuracy which is a harsh metric since you require for each sample that each label set be correctly predicted.

Parameters

Xarray-like of shape (n_samples, n_features)

Test samples.

yarray-like of shape (n_samples,) or (n_samples, n_outputs)

True labels for X.

sample_weightarray-like of shape (n_samples,), default=None

Sample weights.

Returns

scorefloat

Mean accuracy of self.predict(X) wrt. y.

set_params(self, **params)

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters

**paramsdict

Estimator parameters.

Returns

selfobject

Estimator instance.

Examples using skltemplate.TemplateClassifier

Plotting Template Classifier