Sage-Tips

Why do we use Laplace correction in naive Bayes classifier?

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 does the naive Bayes classifier assume the features are?

In simple terms, a Naive Bayes classifier assumes that the presence of a particular feature in a class is unrelated to the presence of any other feature. For example, a fruit may be considered to be an apple if it is red, round, and about 3 inches in diameter.

Is naive Bayes sensitive to irrelevant features?

Not sensitive to irrelevant features. Handles real as well as discrete data. Handles data streaming well.

What is Laplace correction in naive Bayes?

A small-sample correction, or pseudo-count, will be incorporated in every probability estimate. 2. Consequently, no probability will be zero. 3. This is a way of regularizing Naive Bayes, and when the pseudo-count is zero, it is called Laplace smoothing.

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.

What is smoothing 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 …

Why is naive Bayes good for 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.

How does the naive Bayes algorithm work?

Naive Bayes is a kind of classifier which uses the Bayes Theorem. It predicts membership probabilities for each class such as the probability that given record or data point belongs to a particular class. The class with the highest probability is considered as the most likely class.

Why is the naive Bayes classifier called naive what assumption makes it naive?

Naive Bayes is called naive because it assumes that each input variable is independent. This is a strong assumption and unrealistic for real data; however, the technique is very effective on a large range of complex problems.

When estimating probabilities Why do we need smoothing?

Probability smoothing is a language modeling technique that assigns some non-zero probability to events that were unseen in the training data. This has the effect that the probability mass is divided over more events, hence the probability distribution becomes more smooth.

How can we improve the performance of 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.