5. Naive Bayes Classifier

Naive Bayes is based on the Bayes Theorem for calculating probabilities and conditional probabilities
It describes the probability of an event, based on prior knowledge of conditions that might be related to the event

from sklearn import datasets

wine = datasets.load_wine()

print("Features: ", wine.feature_names) # printing the columns of wine dataset

print("Labels: ", wine.target_names) # printing the label type of wine dataset

print(wine.data.shape) # printing the shape of wine dataset

print(wine.data[0:5]) # printing the 5 first rows of wine dataset

print(wine.target)

Features: ['alcohol', 'malic_acid', 'ash', 'alcalinity_of_ash', 'magnesium', 'total_phenols', 'flavanoids', 'nonflavanoid_phenols', 'proanthocyanins', 'color_intensity', 'hue', 'od280/od315_of_diluted_wines', 'proline']

Labels: ['class_0' 'class_1' 'class_2']

Wine data shape: (178, 13)

Wine head: [[1.423e+01 1.710e+00 2.430e+00 1.560e+01 1.270e+02 2.800e+00 3.060e+00 2.800e-01 2.290e+00 5.640e+00 1.040e+00 3.920e+00 1.065e+03] [1.320e+01 1.780e+00 2.140e+00 1.120e+01 1.000e+02 2.650e+00 2.760e+00 2.600e-01 1.280e+00 4.380e+00 1.050e+00 3.400e+00 1.050e+03] [1.316e+01 2.360e+00 2.670e+00 1.860e+01 1.010e+02 2.800e+00 3.240e+00 3.000e-01 2.810e+00 5.680e+00 1.030e+00 3.170e+00 1.185e+03] [1.437e+01 1.950e+00 2.500e+00 1.680e+01 1.130e+02 3.850e+00 3.490e+00 2.400e-01 2.180e+00 7.800e+00 8.600e-01 3.450e+00 1.480e+03] [1.324e+01 2.590e+00 2.870e+00 2.100e+01 1.180e+02 2.800e+00 2.690e+00 3.900e-01 1.820e+00 4.320e+00 1.040e+00 2.930e+00 7.350e+02]]

Target: [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]

from sklearn.model_selection import train_test_split

X_train, X_test, y_train, y_test = train_test_split(wine.data, wine.target, test_size=0.3, random_state=109)

# Generating the model

from sklearn.naive_bayes import GaussianNB

from sklearn import metrics

gnb = GaussianNB()

gnb.fit(X_train, y_train)

y_pred = gnb.predict(X_test)

print("Accuracy: ", metrics.accuracy_score(y_test, y_pred))

Accuracy: 0.9074074074074074

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