ENH Improve speed plot_permutation_tests_for_classification.py
@@ -27,14 +27,14 @@ X = iris.data
y = iris.target
# %%
-# We will also generate some random feature data (i.e., 2200 features),
+# We will also generate some random feature data (i.e., 20 features),
# uncorrelated with the class labels in the iris dataset.
import numpy as np
-n_uncorrelated_features = 2200
+n_uncorrelated_features = 20
rng = np.random.RandomState(seed=0)
-# Use same number of samples as in iris and 2200 features
+# Use same number of samples as in iris and 20 features
X_rand = rng.normal(size=(X.shape[0], n_uncorrelated_features))
# %%
