This step requires building a logistic regression model, and a single neuron hidden layer neural network on some training data. For some reason the predictions for the neural network change depending on whether it is run before the logistic regression, or after it. Is there any reason for this?
lr = LogisticRegression() lr.fit(train_features, train_labels) log_predictions = lr.predict(test_features) mlp = MLPClassifier(hidden_layer_sizes=(1,), activation='logistic') mlp.fit(train_features, train_labels) nn_predictions = mlp.predict(test_features)
I also notice on screen 6, that using different variable names to the solution gives different results. This indicates these may be platform issues?