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

How do 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.

How do you use Laplace smoothing in Naive Bayes?

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 likelihood in Naive Bayes?

Naive Bayes classifier assume that the effect of the value of a predictor (x) on a given class (c) is independent of the values of other predictors. P(x|c) is the likelihood which is the probability of predictor given class. P(x) is the prior probability of predictor.

How many parameters are need for design naive Bayesian classifier?

Therefore, we will need to estimate approximately 2n+1 parameters.

Where can the Bayes rule be used?

Where does the bayes rule can be used? Explanation: Bayes rule can be used to answer the probabilistic queries conditioned on one piece of evidence.

Why Naive Bayes works well with large number of features?

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.

Why do Multinomials naive Bayes?

The term Multinomial Naive Bayes simply lets us know that each p(fi|c) is a multinomial distribution, rather than some other distribution. This works well for data which can easily be turned into counts, such as word counts in text.

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.

Where does the Bayes rule can be used?

How do I build a naive Bayes classifier?

Naive Bayes Tutorial (in 5 easy steps)

1. Step 1: Separate By Class.
2. Step 2: Summarize Dataset.
3. Step 3: Summarize Data By Class.
4. Step 4: Gaussian Probability Density Function.
5. Step 5: Class Probabilities.

What is Bayes Theorem example?

Bayes’ Theorem Example #1 A could mean the event “Patient has liver disease.” Past data tells you that 10% of patients entering your clinic have liver disease. P(A) = 0.10. B could mean the litmus test that “Patient is an alcoholic.” Five percent of the clinic’s patients are alcoholics. P(B) = 0.05.

What is Bayes theorem in simple terms?

: a theorem about conditional probabilities: the probability that an event A occurs given that another event B has already occurred is equal to the probability that the event B occurs given that A has already occurred multiplied by the probability of occurrence of event A and divided by the probability of occurrence of …