Which is better naive Bayes or logistic regression?

Which is better naive Bayes or logistic regression?

Naive Bayes also assumes that the features are conditionally independent. In short Naive Bayes has a higher bias but lower variance compared to logistic regression. If the data set follows the bias then Naive Bayes will be a better classifier.

Can naive Bayes be used for binary classification?

Classification Problems: Naive Bayes is a classification algorithm suitable for binary and multiclass classification. Log Probabilities: The calculation of the likelihood of different class values involves multiplying a lot of small numbers together.

Can naive Bayes be used for regression?

Naive Bayes classifier (Russell, & Norvig, 1995) is another feature-based supervised learning algorithm. It was originally intended to be used for classification tasks, but with some modifications it can be used for regression as well (Frank, Trigg, Holmes, & Witten, 2000) .

Can we use Naive Bayes for multiclass classification?

Naive Bayes is a classification algorithm for binary (two-class) and multiclass classification problems.

What is naive Bayes good for?

Naive Bayes is suitable for solving multi-class prediction problems. If its assumption of the independence of features holds true, it can perform better than other models and requires much less training data. Naive Bayes is better suited for categorical input variables than numerical variables.

Which is a better classifier naive Bayes or logistic regression?

If the data set follows the bias then Naive Bayes will be a better classifier. Both Naive Bayes and Logistic regression are linear classifiers, Logistic Regression makes a prediction for the probability using a direct functional form where as Naive Bayes figures out how the data was generated given the results.

How are discriminative models different from naive Bayes?

In discriminative models, you have “less assumptions”, e.g,. in naive Bayes and classification, you assume that your p (x|y) follows (typically) a Gaussian, Bernoulli, or Multinomial distribution, and you even violate the assumption of conditional independence of the features.

How is logistic regression used in classification problems?

Logistic regression is a linear classification method that learns the probability of a sample belonging to a certain class. Logistic regression tries to find the optimal decision boundary that best separates the classes. 1. Both algorithms are used for classification problems

When to use lasso or ridge logistic regression?

Logistic regression: When the training data size is small relative to the number of features, including regularisation such as Lasso and Ridge regression can help reduce overfitting and result in a more generalised model.