How to gauge overfit with MLPClassifier and cross_val_score? I'm learning sklearn. 

When using MLPClassifier.fit() and MLPClassifier.predict() I would do a manual validation (looking for overfit) by running the training set again through the prediction and accuracy (as follows)...

from sklearn.neural_network import MLPClassifier
from sklearn.metrics import accuracy_score
from sklearn.model_selection import cross_val_score

target = ["target"]
features = [c for c in shuf_df.columns if c not in target]
neurons =[8, 16, 32, 64, 128, 256]
for activation in ["logistic", "relu"]:
    for neuron in neurons:
        mlp = MLPClassifier(hidden_layer_sizes=(neuron,), activation=activation)
        start = datetime.now()
        mlp.fit(train_df[features], train_df[target])
        nn_predictions = mlp.predict(test_df[features])
        accuracy_test = accuracy_score(test_df["target"], nn_predictions)
        nn_predictions = mlp.predict(train_df[features])
        accuracy_train = accuracy_score(train_df["target"], nn_predictions)
        print("({}) [{}] neuron:{}, accuracy_test:{}, accuracy_train:{}".format(datetime.now() - start, 
                                                                                activation, 
                                                                                neuron, 
                                                                                accuracy_test, 
                                                                                accuracy_train
                                                                                )
        )


...which results in...

(0:00:00.750631) [logistic] neuron:8, accuracy_test:0.8611111111111112, accuracy_train:0.9700765483646486 # Probably OK 
(0:00:00.860471) [logistic] neuron:16, accuracy_test:0.8916666666666667, accuracy_train:0.9930410577592206 # Approaching overfit 
(0:00:01.491433) [logistic] neuron:32, accuracy_test:0.8972222222222223, accuracy_train:0.9993041057759221 # Probably overfit 
(0:00:01.951523) [logistic] neuron:64, accuracy_test:0.9166666666666666, accuracy_train:1.0 # overfit 
(0:00:02.449780) [logistic] neuron:128, accuracy_test:0.925, accuracy_train:1.0 # overfit 
(0:00:03.304685) [logistic] neuron:256, accuracy_test:0.925, accuracy_train:1.0 # overfit 
(0:00:00.846773) [relu] neuron:8, accuracy_test:0.8583333333333333, accuracy_train:0.9436325678496869 # Probably OK 
(0:00:00.905262) [relu] neuron:16, accuracy_test:0.8777777777777778, accuracy_train:0.9986082115518441 # Probably overfit 
(0:00:01.531930) [relu] neuron:32, accuracy_test:0.8972222222222223, accuracy_train:1.0 # overfit 
(0:00:01.695193) [relu] neuron:64, accuracy_test:0.9083333333333333, accuracy_train:1.0 # overfit 
(0:00:01.503808) [relu] neuron:128, accuracy_test:0.9027777777777778, accuracy_train:1.0 # overfit 
(0:00:02.060312) [relu] neuron:256, accuracy_test:0.9194444444444444, accuracy_train:1.0 # overfit 


How would I determine overfit when using cross_val_score? (as follows)...

target = ["target"]
features = [c for c in shuf_df.columns if c not in target]
neurons =[8, 16, 32, 64, 128, 256]
for activation in ["logistic", "relu"]:
    for neuron in neurons:
        mlp = MLPClassifier(hidden_layer_sizes=(neuron,), activation=activation)
        cv_scores = cross_val_score(mlp, shuf_df[features], shuf_df[target], cv=4)
        print("[{}] neuron:{}, cv_scores:{}".format(activation,
                                                    neuron,
                                                    cv_scores, 
                                                    )
        )
If I understand your question correctly, you would like the algorithm to detect overfitting by itself, without you manual inspection.

For this you need another split of data. So first you split your entire dataset into training and test, and then you split again your training into two subsets: the actual training and validation. You use these two subsets internally during the learning process to estimate accuracy and detect overfitting (and you may use cross_val_score() for this purpose if you want, just do not touch the holdout test part).

On a side note, looking at your two loops (for activation and neuron), you may want to use Grid Search for this.