Which functions are useful in Naive Bayes' classification when features are continuous?

Question

BlackTom AI · Accepted Answer

Question restatement: Which functions are useful in Naive Bayes' classification when features are continuous?

Option analysis:
- Since the answer_options field is empty, there are no listed alternatives to evaluate here. In a typical Naive Bayes setup with continuous features, the standard approach is to model each feature's distribution per class as Gaussian (Gaussian Naive Bayes). This choice is grounded in the assumption that the continuous feature values in each class follow a normal distribution, which makes the computation of likelihoods tractable: for each feature and class, you estimate the mean and variance from training data and use the Gaussian probability density to compute P(feature | class).

Why Gaussian is a sensible choice: the Gaussian distribution is fully described by two parameters (mean and variance), allowing simple closed-form expressions for likelihoods and easy parameter estimation from data. It also works reasonably well in many practical scenarios where features are approximately continuous and unimodally distributed within each class.

Why this might not always be perfect: if the true feature distributions are far from normal (e.g., highly skewed, multimodal, or bounded), the Gaussian assumption can misrepresent the data and hurt classification accuracy. In such cases, other approaches within the Naive Bayes family or preprocessing steps can help, such as:
- Using a different continuous-distribution assumption (e.g., Laplace, Gamma) if domain knowledge suggests it fits better.
- Applying kernel density estimates or Parzen windows to model P(feature | class) more flexibly, at the cost of more computation and possibly less robustness with small datasets.
- Discretizing continuous features into bins and using a multinomial or categorical NB, although this sacrifices some information.

Overall takeaway: for continuous features in Naive Bayes, Gaussian (Gaussian Naive Bayes) is the conventional and widely used choice due to mathematical convenience and good empirical performance in many cases, with alternatives available when data do not conform to normality.

Machine Learning: Fundamentals and Applications Quiz 3 linear classification 2

Which functions are useful in Naive Bayes' classification when features are continuous?

View Explanation

Log in for full answers

Similar Questions

In a consumer society, many adults channel creativity into buying things

Economic stress and unpredictable times have resulted in a booming industry for self-help products

People born without creativity never can develop it

A product has a selling price of $20, a contribution margin ratio of 40% and fixed cost of $120,000. To make a profit of $30,000. The number of units that must be sold is: Type the number without $ and a comma. Eg: 20000

Which of the following statement regarding cost is correct:

Under the assumptions used in cost-volume-profit analysis, as the activity increases:

The CVP and break-even analysis are supported by a range of important assumptions. Which of the following is not an assumption of the CVP:

The break-even point is that at which:

More Practical Tools for Students Powered by AI Study Helper

Homework AI Solver

Stylized AI Paper Writer

Plagiarism Checker Assistant

Citation AI Academic Writing Tool

In-Class Translation Assistant

AI Note Generator

AI Quiz Answers

Past Exam Questions from University Test Bank

Smart Practice Assistant

Adaptive Practice

Making Your Study Simpler