Delete 3217-classification-lr-example2.py

3217-classification-lr-example2.py

-from sklearn import datasets, neighbors, linear_model
-from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay, f1_score
-import matplotlib.pyplot as plt
-
-X_digits, y_digits = datasets.load_digits(return_X_y=True)
-X_digits = X_digits / X_digits.max()
-
-n_samples = len(X_digits)
-
-ratio = 0.9
-print (n_samples)
-#train data
-X_train = X_digits[: int(ratio * n_samples)]
-y_train = y_digits[: int(ratio * n_samples)]
-print (X_train.shape)
-
-
-#test data
-
-X_test = X_digits[int(ratio * n_samples) :]
-y_test = y_digits[int(ratio * n_samples) :]
-print (X_test.shape)
-
-logistic = linear_model.LogisticRegression(max_iter=1000)
-
-print(
-    "LogisticRegression score: %f"
-    % logistic.fit(X_train, y_train).score(X_test, y_test))
-
-#Get results on actual test labels and predicted labels
-
-predictions = logistic.predict(X_test)
-
-#print (predictions)
-#print (y_test)
-#get f1 score
-
-print (f1_score(y_test, predictions, average='macro'))
-
-#get confusion matrix
-cm = confusion_matrix(y_test, predictions, labels=logistic.classes_)
-disp = ConfusionMatrixDisplay(confusion_matrix=cm,                           display_labels=logistic.classes_)
-disp.plot()
-plt.show()