Breast Cancer Classification with Naive Bayes and Class-Specific Feature Weighting in Python

This is an example of how to use Naive Bayes with class-specific feature weighting in Python to classify the Breast-Cancer dataset:

import numpy as np
from sklearn import datasets
from sklearn.naive_bayes import GaussianNB

# Load the Breast-Cancer dataset
breast_cancer = datasets.load_breast_cancer()

# Extract the data and target variable
X = breast_cancer.data
y = breast_cancer.target

# Calculate weights for each feature by calculating the mean and standard deviation of feature values for each class
weights = []
for i in range(X.shape[1]):
    mean_0 = np.mean(X[y == 0, i])
    std_0 = np.std(X[y == 0, i])
    mean_1 = np.mean(X[y == 1, i])
    std_1 = np.std(X[y == 1, i])
    weight = (mean_1 - mean_0) / (std_0 + std_1)
    weights.append(weight)

# Weight each sample's features
X_weighted = X * np.array(weights)

# Use Naive Bayes classifier for classification
gnb = GaussianNB()
gnb.fit(X_weighted, y)
y_pred = gnb.predict(X_weighted)

# Output classification results
print('Classification accuracy: {:.2f}%'.format(np.mean(y_pred == y) * 100))

The output may vary due to randomness, but the classification accuracy should be above 90%.