#Three lines to make our compiler able to draw: import sys import matplotlib matplotlib.use('Agg') import matplotlib.pyplot as plt from sklearn import datasets from sklearn.model_selection import train_test_split from sklearn.metrics import accuracy_score from sklearn.ensemble import BaggingClassifier data = datasets.load_wine() X = data.data y = data.target X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 22) estimator_range = [2,4,6,8,10,12,14,16] models = [] scores = [] for n_estimators in estimator_range: # Create bagging classifier clf = BaggingClassifier(n_estimators = n_estimators, random_state = 22) # Fit the model clf.fit(X_train, y_train) # Append the model and score to their respective list models.append(clf) scores.append(accuracy_score(y_true = y_test, y_pred = clf.predict(X_test))) # Generate the plot of scores against number of estimators plt.figure(figsize=(9,6)) plt.plot(estimator_range, scores) # Adjust labels and font (to make visable) plt.xlabel("n_estimators", fontsize = 18) plt.ylabel("score", fontsize = 18) plt.tick_params(labelsize = 16) # Visualize plot plt.show() #Two lines to make our compiler able to draw: plt.savefig(sys.stdout.buffer) sys.stdout.flush()