Challenge Solution Review
In this lesson, we explain the solution to the last challenge lesson.
import sklearn.datasets as datasetsfrom sklearn.neural_network import MLPClassifierfrom sklearn.model_selection import train_test_splitimport sklearn.metrics as metricsX, y = datasets.make_classification(n_samples=1000,n_features=30,random_state=10)train_x, test_x, train_y, test_y = train_test_split(X,y,test_size=0.2,random_state=42)nn = MLPClassifier(batch_size=32,hidden_layer_sizes=(64, 32),solver="sgd",shuffle=True,tol=1e-3,max_iter=500,learning_rate_init=0.0001, random_state=13)nn.fit(train_x, train_y)pred_y = nn.predict(test_x)f1 = metrics.f1_score(y_true=test_y, y_pred=pred_y)print("The F1 score is {}.".format(f1))
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