User Guide

Make a classification problem

from score_regression import ScoreRegression
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split
from sklearn.metrics import roc_auc_score
import numpy as np

seed = 43
X, y = make_classification(
    n_samples=100,
    n_features=5,
    n_informative=2,
    n_redundant=2,
    n_classes=2,
    random_state=seed
)
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=seed)

Train the classifier

cls = ScoreRegression().fit(X_train, y_train)

Get the score on unseen data

cls.score(X_test, y_test)

0.88

Class probabilities

We vertically stack the ground truth on the top with the probabilities of class 1 on the bottom. The first five entries are shown.

np.round(np.vstack((y_train, cls.predict_proba(X_train).T))[:, 0:5], 2)

array([[0.  , 1.  , 1.  , 0.  , 0.  ],
       [0.65, 0.39, 0.3 , 0.96, 0.83],
       [0.35, 0.61, 0.7 , 0.04, 0.17]])

roc_auc_score(y_true=y_train, y_score=cls.predict_proba(X_train)[:, 1])

0.9544159544159544

Predict the classes

The ground truth is on the top and the predicted class is on the bottom. The first column is the index. The first five entries are shown.

y_pred = cls.predict(X_test)
np.vstack((y_test, y_pred))[:, 0:5]

array([[1, 0, 0, 0, 1],
       [1, 0, 0, 0, 1]])

The class prediction is expected to be lower than the auc prediction.

roc_auc_score(y_true=y_test, y_score=y_pred)

0.8733766233766235