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()
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