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Why does logistic regression perform better than naive Bayes?
Naive Bayes also assumes that the features are conditionally independent. Real data sets are never perfectly independent but they can be close. 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.
Is logistic regression faster than naive Bayes?
I can’t find a reference now, but e.g. in classification, naive Bayes converges quicker but has typically a higher error than logistic regression. On small datasets you’d might want to try out naive Bayes, but as your training set size grows, you likely get better results with logistic regression.
What is the major difference between naive Bayes and logistic regression?
Naïve Bayes has a naive assumption of conditional independence for every feature, which means that the algorithm expects the features to be independent which not always is the case. Logistic regression is a linear classification method that learns the probability of a sample belonging to a certain class.
Can we use Naive Bayes 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) .
How do I remove overfitting in logistic regression?
One of the ways to combat over-fitting is to increase the training data size. Let take the case of MNIST data set trained with 5000 and 50000 examples,using similar training process and parameters. we can observe that training and validation errors steadily decrease during the initial part of the learning process.
What’s the difference between naive Bayes and logistic regression?
Naïve Bayes is a classification method based on Bayes’ theorem that derives the probability of the given feature vector being associated with a label. Naïve Bayes has a naive assumption of conditional independence for every feature, which means that the algorithm expects the features to be independent which not always is the case.
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.