(Answered): Here's what you have to do:(1) Use Scikit learn wine dataset (sklearn.datasets.load_wine())and plot ...

Logistic Regression With Iris Data [] from sklearn.linear_model import LogisticRegression clf = LogisticRegression (random_state=0).fit(X_train, y_train) clf.predict(X_test) clf.predict_proba(x_test) clf.score(X_test, y_test) 0.96 import sklearn.metrics as metrics predicted = clf.predict(X_test) print("Classification report:") print(metrics.classification_report(y_test, predicted)) Accura Classification report: precision recall f1-score support 1 2 1.00 1.00 0.79 1.00 0.83 1.00 1.00 0.91 0.88 16 18 11 micro avg macro avg weighted avg 0.93 0.93 0.95 0.93 0.94 0.93 0.93 0.93 0.93 45 45 45 Random Forest With Digits data [] from sklearn.ensemble import RandomForestClassifier classifier = RandomForestclassifier() X_train, x_test, y_train, y_test = train_test_split(data, digits.target, test_size=0.5, shuffle=False) classifier = classifier.fit(X_train, y_train) [] predicted = classifier.predict(X_test) print(classifier.score (X_test,y_test)) 0.8765294771968855 [] - axes = plt.subplots(1, 4) images_and_predictions = list(zip(digits.images[n_samples // 2:], predicted)) for ax, (image, prediction) in zip(axes, images_and_predictions [:4]): ax.set_axis_off() ax.imshow(image, cmap=plt.cm.gray_r, interpolation='nearest') ax.set_title('Prediction: %i' % prediction) Prediction: 8 Prediction: 6 Prediction: 4 Prediction: 9