How can we improve Naive Bayes prediction?

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How can we improve Naive Bayes prediction?

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.

Why Naive Bayes is a bad estimator?

On the other side naive Bayes is also known as a bad estimator, so the probability outputs are not to be taken too seriously. Another limitation of Naive Bayes is the assumption of independent predictors. In real life, it is almost impossible that we get a set of predictors which are completely independent.

Why is Naive Bayes algorithm considered as generative model although it appears that it calculates conditional probability distribution?

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This approach generally requires more sophisticated probabilistic thinking than a regression mentality demands, but it provides a complete model of the probabilistic structure of the data. Knowing the joint distribution enables you to generate the data; hence, Naive Bayes is a generative model.

How do I fix naive Bayes?

3. Ways to Improve Naive Bayes Classification Performance

  1. 3.1. Remove Correlated Features.
  2. 3.2. Use Log Probabilities.
  3. 3.3. Eliminate the Zero Observations Problem.
  4. 3.4. Handle Continuous Variables.
  5. 3.5. Handle Text Data.
  6. 3.6. Re-Train the Model.
  7. 3.7. Parallelize Probability Calculations.
  8. 3.8. Usage with Small Datasets.

Is naive Bayes good for NLP?

It has been successfully used for many purposes, but it works particularly well with natural language processing (NLP) problems. Naive Bayes is a family of probabilistic algorithms that take advantage of probability theory and Bayes’ Theorem to predict the tag of a text (like a piece of news or a customer review).

What are the limitations of using naive Bayes algorithm to detect spam?

Disadvantages – A subtle issue with Naive-Bayes Classifier is that if you have no occurrences of a class label and a certain attribute value together then the frequency-based probability estimation will be zero. A big data set is required for making reliable predictions of the probability of each class.

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Why is naive Bayes better than 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.

Is naive Bayes discriminative or generative?

Naive bayes is a Generative model whereas Logistic Regression is a Discriminative model . Generative model is based on the joint probability, p( x, y), of the inputs x and the label y, and make their predictions by using Bayes rules to calculate p(y | x), and then picking the most likely label y.

What is naive Bayes theorem in NLP?

Bayes theorem calculates probability P (c|x) where c is the class of the possible outcomes and x is the given instance which has to be classified, representing some certain features. P (c|x) = P (x|c) * P (c) / P (x) Naive Bayes are mostly used in natural language processing (NLP) problems. Naive Bayes predict the tag of a text.

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What is naivenaive Bayes classifier algorithm?

Naive Bayes Classifier Algorithm is a family of probabilistic algorithms based on applying Bayes’ theorem with the “naive” assumption of conditional independence between every pair of a feature.

How do you find the naive Bayes probability?

To calculate the Naive Bayes probability, P ( d | c ) x P ( c ), we calculate P ( xi | c ) for each xi in d, and multiply them together. Then we multiply the result by P ( c ) for the current class. We do this for each of our classes, and choose the class that has the maximum overall value.

What is the difference between naive Bayes and multinomial Bayes?

For difference between Naive Bayes & Multinomial Naive Bayes: Naive Bayes is generic. Multinomial Naive Bayes is a specific instance of Naive Bayes where the P (Feature i |Class) follows multinomial distribution (word counts, probabilities, etc.)