Quick Start
from sppam import SPPAM
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_splitMake a classification problem
seed = 42
X, y = make_classification(
n_samples=30,
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 = SPPAM().fit(X_train, y_train)Get the score on unseen data
cls.score(X_test, y_test)1.0
