What is a naive Bayes classifier?

Naive Bayes classifier is a simple probabilistic classifier based on applying Bayes' theorem with the assumption of independence between features.

Idea:

• Calculate the probability of an object belonging to a class, considering feature values.
• Assume all features are independent, which simplifies calculations.

Example: classifying emails as spam or not spam based on words in the text.

Advantages:

• Fast and easy to implement.
• Works well on large datasets.

Limitations:

• The assumption of feature independence is often not met, which can reduce accuracy.

Example in Python using sklearn:

from sklearn.naive_bayes import MultinomialNB
from sklearn.feature_extraction.text import CountVectorizer

texts = ["spam message", "important meeting", "buy now", "project update"]
labels = [1, 0, 1, 0]  # 1 - spam, 0 - not spam

vectorizer = CountVectorizer()
X = vectorizer.fit_transform(texts)

model = MultinomialNB()
model.fit(X, labels)

new_text = ["buy meeting"]
X_new = vectorizer.transform(new_text)
prediction = model.predict(X_new)
print(prediction)  # Will output [1] - spam