watex.utils.plotutils.plot_confusion_matrix

watex.utils.plotutils.plot_confusion_matrix(yt, y_pred, view=True, ax=None, annot=True, **kws)

plot a confusion matrix for a single classifier model.

:param ytndarray or Series of length n

An array or series of true target or class values. Preferably, the array represents the test class labels data for error evaluation.

Parameters

• y_pred – ndarray or Series of length n An array or series of the predicted target.

• view – bool, default=True Option to display the matshow map. Set to False mutes the plot.

• annot – bool, default=True Annotate the number of samples (right or wrong prediction ) in the plot. Set False to mute the display.

param kws: dict,

Additional keyword arguments passed to the function sckitlearn.metrics.confusion_matrix().

Returns

mat- confusion matrix bloc matrix

Example

>>> #Import the required models and fetch a an Ababoost model
>>> # for instance then plot the confusion metric
>>> import matplotlib.pyplot as plt
>>> plt.style.use ('classic')
>>> from watex.datasets import fetch_data
>>> from watex.exlib.sklearn import train_test_split
>>> from watex.models import pModels
>>> from watex.utils.plotutils import plot_confusion_matrix
>>> # split the  data . Note that fetch_data output X and y
>>> X, Xt, y, yt  = train_test_split (* fetch_data ('bagoue analysed'),
                                      test_size =.25  )
>>> # train the model with the best estimator
>>> pmo = pModels (model ='ada' )
>>> pmo.fit(X, y )
>>> print(pmo.estimator_ )
>>> #%%
>>> # Predict the score using under the hood the best estimator
>>> # for adaboost classifier
>>> ypred = pmo.predict(Xt)
>>> # now plot the score
>>> plot_confusion_matrix (yt , ypred )