watex.models.naive_evaluation#

watex.models.naive_evaluation(clf, X, y, cv=7, scoring='accuracy', display='off', **kws)[source]#

Quick scores evaluation using cross validation.

Parameters

  • clf (callable) – Classifer for testing default data.

  • X (ndarray) – trainset data

  • y (array_like) – label data

  • cv (int) – KFold for data validation.

  • scoring (str) – type of error visualization.

  • display (str or bool,) – show the show on the stdout

  • kws (dict,) – Additional keywords arguments passed to watex.exlib.slearn.cross_val_score().

Returns

scores, mean_core – scaore after evaluation and mean of the score

Return type

array_like, float

Examples

>>> import watex as wx
>>> from watex.models.validation import naive_evaluation
>>> X,  y = wx.fetch_data ('bagoue data prepared')
>>> clf = wx.sklearn.DecisionTreeClassifier()
>>> naive_evaluation(clf, X, y , cv =4 , display ='on' )
clf=: DecisionTreeClassifier
scores=: [0.6279 0.7674 0.7093 0.593 ]
scores.mean=: 0.6744186046511629
Out[57]: (array([0.6279, 0.7674, 0.7093, 0.593 ]), 0.6744186046511629)