Dear sklearn users, I did some supervised classification simulations with the make_classification function from sklearn increasing the number of informative features from 1 out of 40 to 40 out of 40 (100%). I did not generate any repeated or redundant features. I fixed the number of classes to two and the number of clusters per class to one. I split the dataset 100 times using the StratifiedShuffleSplit function into two subsets: a training set and a test set (80% - 20%). I performed a logistic regression and calculated training and testing accuracies and averaged the results over the 100 splits leading to a mean training accuracy and a mean testing accuracy. I was expecting to get an increasing accuracy score as a function of informative features for both the training and the test sets. On the contrary, I have got the best training and test scores for one informative feature. Why do I get these results ? Thanks for your help, Best regards, Ben Below the simulation code I have written: import numpy as np from sklearn.datasets import make_classification from sklearn.model_selection import StratifiedShuffleSplit from sklearn.preprocessing import StandardScaler from sklearn.linear_model import LogisticRegression from sklearn.metrics import accuracy_score import matplotlib.pyplot as plt RANDOM_SEED = 4 n_inf = np.array([1, 5, 10, 15, 20, 25, 30, 35, 40]) mean_training_score_array = np.array([]) mean_testing_score_array = np.array([]) for n_inf_value in n_inf:     X, y = make_classification(n_samples=2500,                                n_features=40,                                n_informative=n_inf_value,                                n_redundant=0,                                n_repeated=0,                                n_classes=2,                                n_clusters_per_class=1,                                random_state=RANDOM_SEED,                                shuffle=False)     #     print('Simulated data - number of informative features = ' + str(n_inf_value))     #     sss = StratifiedShuffleSplit(n_splits=100, test_size=0.2, random_state=RANDOM_SEED)     training_score_array = np.array([])     testing_score_array = np.array([])     for train_index_split, test_index_split in sss.split(X, y):         X_split_train, X_split_test = X[train_index_split], X[test_index_split]         y_split_train, y_split_test = y[train_index_split], y[test_index_split]         scaler = StandardScaler()         X_split_train = scaler.fit_transform(X_split_train)         X_split_test = scaler.transform(X_split_test)         lr = LogisticRegression(fit_intercept=True, max_iter=1e9, verbose=0,                                 random_state=RANDOM_SEED, solver='lbfgs', tol=1e-6, C=10)         lr.fit(X_split_train, y_split_train)         y_pred_train = lr.predict(X_split_train)         y_pred_test = lr.predict(X_split_test)         accuracy_train_score = accuracy_score(y_split_train, y_pred_train)         accuracy_test_score = accuracy_score(y_split_test, y_pred_test)         training_score_array = np.append(training_score_array, accuracy_train_score)         testing_score_array = np.append(testing_score_array, accuracy_test_score)     mean_training_score_array = np.append(mean_training_score_array, np.average(training_score_array))     mean_testing_score_array = np.append(mean_testing_score_array, np.average(testing_score_array)) # print('mean_training_score_array=' + str(mean_training_score_array)) print('mean_testing_score_array=' + str(mean_testing_score_array)) # plt.plot(n_inf, mean_training_score_array, 'r', label='mean training score') plt.plot(n_inf, mean_testing_score_array, 'g', label='mean testing score') plt.xlabel('number of informative features out of 40') plt.ylabel('accuracy') plt.legend() plt.show()