Is Naive Bayes good for image classification?

Naive Bayes Nearest Neighbor (NBNN) has been proposed as a powerful, learning-free, non-parametric approach for object classification. Its good performance is mainly due to the avoidance of a vector quantization step, and the use of image-to-class comparisons, yielding good generalization.

What is the best classifier for image classification?

Convolutional Neural Networks (CNNs) is the most popular neural network model being used for image classification problem. The big idea behind CNNs is that a local understanding of an image is good enough.

What are the disadvantages of using a Naive Bayes for classification?

Disadvantages of Using Naive Bayes Classifier In most situations, the feature show some form of dependency. Zero probability problem : When we encounter words in the test data for a particular class that are not present in the training data, we might end up with zero class probabilities.

Why is Naive Bayes good for sentiment analysis?

Multinomial Naive Bayes classification algorithm tends to be a baseline solution for sentiment analysis task. The basic idea of Naive Bayes technique is to find the probabilities of classes assigned to texts by using the joint probabilities of words and classes. To avoid underflow, log probabilities can be used.

Is Naive Bayes good for sentiment analysis?

Naive Bayes Classifier Overview Naive Bayes is the simplest and fastest classification algorithm for a large chunk of data. In various applications such as spam filtering, text classification, sentiment analysis, and recommendation systems, Naive Bayes classifier is used successfully.

What are the advantages of naive Bayes algorithm?

Advantages of Naive Bayes Classifier It handles both continuous and discrete data. It is highly scalable with the number of predictors and data points. It is fast and can be used to make real-time predictions. It is not sensitive to irrelevant features.