Description
The wine dataset is a multi-class classification dataset which contains three different wine categories and continuous-valued
features, for a total of observations.
The goal is to classify an unlabeled wine according to its characteristic features.
13
178
In [2]:
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 .
X.shape = (178, 13)
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 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 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 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’]
from sklearn import datasets
dataset = datasets.load_wine()
X = dataset.data
y = dataset.target
print(“\nX.shape =”, X.shape)
print(“\ny.shape =”, y.shape)
print(“\nwine categories:\n”, dataset[‘target’])
print(“\nfeatures names:\n”, dataset[‘feature_names’])
