[Solved] PML Homework 05

The wine dataset is a multi-class classification dataset which contains three different wine categories and 13 continuous-valued features, for a total of 178 observations.

The goal is to classify an unlabeled wine according to its characteristic features.

y.shape = (178,)

wine categories:

[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

• 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
• 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
• 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
• 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2]

features names:

[alcohol, malic_acid, ash, alcalinity_of_ash, magnesium, total_phenols, flavanoids , nonflavanoid_phenols, proanthocyanins, color_intensity, hue, od280/od315_of_diluted_w ines, proline]

1. Perform a train-test split on the data using sklearn train_test_split with test_size=0.3 . Name your variables X_train , X_test , y_train , y_test . Make sure that your training set contains samples from all the categories.
2. Fit sklearn LogisticRegression model to the training data X_train , y_train , predict the classification labels on the test data X_test and use sklearn classification_report to evaluate your model against the actual labels y_test .
3. Repeat step 2. using sklearn Naive Bayes classifier GaussianNB .