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What is importance of Laplacian smoothing in text classification?

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What is importance of Laplacian smoothing in text classification?

Laplace smoothing is a smoothing technique that helps tackle the problem of zero probability in the Naïve Bayes machine learning algorithm. Using higher alpha values will push the likelihood towards a value of 0.5, i.e., the probability of a word equal to 0.5 for both the positive and negative reviews.

What is Laplace smoothing in R?

It allows numeric and factor variables to be used in the naive bayes model. Laplace smoothing allows unrepresented classes to show up. Predictions can be made for the most likely class or for a matrix of all possible classes.

Is Laplace smoothing regularization?

We now introduce Laplace smoothing, a technique for smoothing categorical data. This is a way of regularizing Naive Bayes, and when the pseudo-count is zero, it is called Laplace smoothing.

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Why is smoothing useful when applying naive Bayes?

Why is “smoothing” useful when applying Naive Bayes? Smoothing allows Naive Bayes to better handle cases where there are many categories to classify between, instead of just two. Smoothing allows Naive Bayes to turn a conditional probability of evidence given a category into a probability of a category given evidence.

What is additive smoothing in NLP?

In a bag of words model of natural language processing and information retrieval, the data consists of the number of occurrences of each word in a document. Additive smoothing allows the assignment of non-zero probabilities to words which do not occur in the sample.

Why is smoothing needed NLP?

Smoothing techniques in NLP are used to address scenarios related to determining probability / likelihood estimate of a sequence of words (say, a sentence) occuring together when one or more words individually (unigram) or N-grams such as bigram(wi/wi−1) or trigram (wi/wi−1wi−2) in the given set have never occured in …

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How do you regularize 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.