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Why is naive Bayes good for small datasets?
Because of the class independence assumption, naive Bayes classifiers can quickly learn to use high dimensional features with limited training data compared to more sophisticated methods. This can be useful in situations where the dataset is small compared to the number of features, such as images or texts.
Is naive Bayes good for small data?
Naive Bayesian models are easy to build and particularly useful for small & medium sized data sets (the one used in this article is evidence of that!). Along with simplicity, Naive Bayes is known to outperform even highly sophisticated classification methods.
Why naive Bayes is best for NLP?
Naive Bayes are mostly used in natural language processing (NLP) problems. Naive Bayes predict the tag of a text. They calculate the probability of each tag for a given text and then output the tag with the highest one.
Why naive Bayes works well with large data?
Its key benefits are its simplicity, efficiency, ability to handle noisy data and for allowing multiple classes of classification3. It also doesn’t require a large amount of data to work well. Another important benefit of naive Bayes is that it is robust to missing data.
Why is Naive Bayes good?
Pros: It is easy and fast to predict class of test data set. It also perform well in multi class prediction. When assumption of independence holds, a Naive Bayes classifier performs better compare to other models like logistic regression and you need less training data.
Why is naive Bayes good?
What is the benefit of naive Bayes?
Advantages of Naive Bayes Classifier It is simple and easy to implement. It doesn’t require as much training data. It handles both continuous and discrete data. It is highly scalable with the number of predictors and data points.
Why is Naive Bayes good for classification?
It is easy and fast to predict class of test data set. It also perform well in multi class prediction. When assumption of independence holds, a Naive Bayes classifier performs better compare to other models like logistic regression and you need less training data.
Why does Naive Bayes work well on text classification?
As the Naive Bayes algorithm has the assumption of the “Naive” features it performs much better than other algorithms like Logistic Regression, Tree based algorithms etc. The Naive Bayes classifier is much faster with its probability calculations.