Loading...

verticapy.machine_learning.memmodel.linear_model.LinearModel.predict_proba#

LinearModel.predict_proba(X: list | ndarray) → ndarray#

Computes the model’s probabilites using the input matrix.

Parameters#

X: ArrayLike

The data on which to make the prediction.

Returns#

numpy.array

Probabilities.

Examples#

Import the required module.

from verticapy.machine_learning.memmodel.linear_model import LinearModelClassifier

We will use the following attributes:

coefficients = [0.5, 1.2]

intercept = 2.0

Let’s create a model.

model_lm = LinearModelClassifier(coefficients, intercept)

Create a dataset.

data = [[1.0, 0.3], [2.0, -0.6]]

Compute the predictions.

model_lm.predict_proba(data)
Out[6]: 
array([[0.0541667 , 0.9458333 ],
       [0.09279295, 0.90720705]])

Note

Refer to LinearModelClassifier for more information about the different methods and usages.