"X, y = make_classification(1000, 20, n_informative=10, random_state=0)\n",
"X, y = X.astype(np.float32), y.astype(np.int64)"
@@ -236,23 +236,6 @@
"As in `sklearn`, we call `fit` passing the input data `X` and the targets `y`. By default, `NeuralNetClassifier` makes a `StratifiedKFold` split on the data (80/20) to track the validation loss. This is shown, as well as the train loss and the accuracy on the validation set."