Naive Bayes Classification

Naive Bayes classification is a probabilistic classifier that applies Bayes' theorem under the simplifying assumption that predictors are conditionally independent given the class, and assigns each case to the most probable class.

나이브 베이즈 분류는 클래스가 주어지면 예측변수들이 서로 조건부 독립이라는 단순화 가정 아래 베이즈 정리를 적용하여 각 사례를 가장 확률이 높은 클래스에 배정하는 확률적 분류기입니다.

What is a simple and fast way to classify cases into categories from several predictors?

여러 예측변수로 사례를 범주에 분류하는 간단하고 빠른 방법은 무엇인가요?

Naive Bayes estimates the probability of each class from the predictors by assuming they are independent within each class, which makes it fast and effective even with small training sets or many features. It is a common baseline for text classification and fits labelled data where speed matters more than well-calibrated probabilities.

When to use Naive Bayes Classification

• Text classification with word counts or binary word features
• Many predictors with a relatively small labelled training set
• Quick baseline classifier before trying more complex models

When to use something else

• Correlated predictors and well-calibrated probabilities are needed: use logistic regression
• Complex interactions among predictors: use random forest or gradient boosting

Assumptions and requirements

• Labelled training data for each class
• Predictors approximately conditionally independent given the class
• Likelihood matched to the data (Gaussian, multinomial, or Bernoulli)
• Smoothing to handle feature values unseen in a class

What the analysis reports

• Predicted classes and class probabilities
• Confusion matrix
• Accuracy, precision, recall, and F1 score
• ROC curve and AUC

How to report it

The naive Bayes classifier achieved a cross-validated accuracy of [value] (SD [value]) and a macro-averaged F1 score of [value].

Related methods

• Logistic Regression
• Support Vector Machine
• Random Forest

Run Naive Bayes Classification on Quorant