memModel.predict¶

In [ ]:

memModel.predict(X: list)

Predicts using the model's attributes.

Parameters¶

Name	Type	Optional	Description
X	list / numpy.array	❌	data.

Returns¶

numpy.array : Predicted values.

Example¶

In [13]:

from verticapy.learn.memmodel import memModel
model = memModel("BinaryTreeClassifier", {"children_left": [1, 3, None, None, None], 
                                          "children_right": [2, 4, None, None, None],
                                          "feature": [0, 1, None, None, None],
                                          "threshold": ['female', 30, None, None, None],
                                          "value": [None, None, [0.8, 0.1, 0.1], [0.1, 0.8, 0.1], [0.2, 0.2, 0.6]],
                                          "classes": ['a', 'b', 'c',]})
model.predict([['male', 100], 
               ['female', 20] , 
               ['female', 50]])

Out[13]:

array(['a', 'b', 'c'], dtype='<U1')