watex.view.EvalPlot.plotConfusionMatrix

EvalPlot.plotConfusionMatrix(clf, *, kind=None, labels=None, matshow_kws=None, **conf_mx_kws)

Plot confusion matrix for error evaluation.

A representation of the confusion matrix for error visualization. If kind is set map, plot will give the number of confused instances/items. However when kind is set to error, the number of items confused is explained as a percentage.

Parameters:

clf (callable, always as a function, classifier estimator) – A supervised predictor with a finite set of discrete possible output values. A classifier must supports modeling some of binary, targets. It must store a classes attribute after fitting.

labels: int, or list of int, optional

Specific class to evaluate the tradeoff of precision

and recall. label needs to be specified and a value within the target.

plottype: str

can be map or error to visualize the matshow of prediction and errors respectively.

matshow_kws: dict

matplotlib additional keywords arguments.

conf_mx_kws: dict

Additional confusion matrix keywords arguments.

ylabel: list

list of labels names to hold the name of each categories. Return

Examples

>>> from watex.datasets import fetch_data
>>> from watex.utils.mlutils import cattarget
>>> from watex.exlib.sklearn import SVC
>>> from watex.view.mlplot import EvalPlot
>>> X, y = fetch_data ('bagoue', return_X_y=True, as_frame =True)
>>> # partition the target into 4 clusters-> just for demo
>>> b= EvalPlot(scale =True, label_values = 4 )
>>> b.fit_transform (X, y)
>>> # prepare our estimator
>>> svc_clf = SVC(C=100, gamma=1e-2, kernel='rbf', random_state =42)
>>> matshow_kwargs ={
'aspect': 'auto', # 'auto'equal
'interpolation': None,
'cmap':'jet }
>>> plot_kws ={'lw':3,
'lc':(.9, 0, .8),
'font_size':15.,
'cb_format':None,
'xlabel': 'Predicted classes',
'ylabel': 'Actual classes',
'font_weight':None,
'tp_labelbottom':False,
'tp_labeltop':True,
'tp_bottom': False
}
>>> b.plotConfusionMatrix(clf=svc_clf,
matshow_kws = matshow_kwargs,
**plot_kws)
>>> svc_clf = SVC(C=100, gamma=1e-2, kernel='rbf',
...                  random_state =42)
>>> # replace the integer identifier with litteral string
>>> b.litteral_classes = ['FR0', 'FR1', 'FR2', 'FR3']
>>> b.plotConfusionMatrix(svc_clf, matshow_kws=matshow_kwargs,
kind='error', **plot_kws)