import numpy as np from sklearn.linear_model import LinearRegression from sklearn.linear_model.logistic import LogisticRegression from sklearn.model_selection import KFold # from sklearn.metrics.regression import import matplotlib.pyplot as plt model = LogisticRegression() arr = np.array([2, 3, 4, 5, 6]) X = np.array([[1, 2], [3, 4], [4, 5], [4, 5], [4, 5], [4, 5], [4, 5], [4, 5]]) # y = np.ones(X.shape[0]) y = np.array([0, 1, 0, 0, 1, 1, 1, 0]) n_fold = KFold(n_splits=2) kf = n_fold.get_n_splits(X, y) print(n_fold) for train_idx, test_idx in n_fold.split(X, y): X_train = X[train_idx] X_test = X[test_idx] y_train = y[train_idx] y_test = y[test_idx] model.fit(X_train, y_train) pred = model.predict([[6, 6]]) print(model.get_params()) print(pred) print(model.coef_) print(model.intercept_)