How can you increase the accuracy of a naive Bayes classifier?

How can you increase the accuracy of a naive Bayes classifier?

Better Naive Bayes: 12 Tips To Get The Most From The Naive Bayes Algorithm

1. Missing Data. Naive Bayes can handle missing data.
2. Use Log Probabilities.
3. Use Other Distributions.
4. Use Probabilities For Feature Selection.
5. Segment The Data.
6. Re-compute Probabilities.
7. Use as a Generative Model.
8. Remove Redundant Features.

What is the benefit of Naive Bayes requires less training data?

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.

Is it possible to overfit naive Bayes with data?

Naive Bayes has shown to perform well on document classification, but that doesn’t mean that it cannot overfit data. There is a difference between the task, document classification, and the data.

When to use naive Bayes instead of logistic regression?

Given that interactions between attributes are ignored in the model, we do not need examples of these interactions and therefore generally less data than other algorithms, such as logistic regression. Further, it is less likely to overfit the training data with a smaller sample size. Try Naive Bayes if you do not have much training data. 11.

Is your train / test split bad naive Bayes?

Overfitting can happen even if Naive Bayes is implemented properly. Is my train/test split bad? I’ve tried splitting the data in different ways, but it does not seem to make a difference. Right now I’m splitting the data by placing a random 90% sample of documents into the train set and the rest into the test set – separately for each label.

Which is the best parameter for naive Bayes classifier?

Naive Bayes classifiers has limited options for parameter tuning like alpha=1 for smoothing, fit_prior= [True|False] to learn class prior probabilities or not and some other options (look at detail here ). I would recommend to focus on your pre-processing of data and the feature selection.