Why is Laplace smoothing important for 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’s the reason for high bias or variance?
If the average prediction values are significantly different from the true value based on the sample data, the model has a high level of bias. Every algorithm starts with some level of bias, because bias results from assumptions in the model that make the target function easier to learn.
Why do we need Laplacian smoothing in a naive Bayes?
An LDA classifier is Bayes-optimal (ignoring estimation error) if the distributions corresponding to the two classes are Gaussian and have equal covariance. Is laplacian smoothing (+1 smoothing) required for continuous data as well in Gaussain Naive Bayes?
Why does the naive Bayes classifier have bias?
The Naive Bayes classifier employs a very simple (linear) hypothesis function, the function it uses to model data. It suffers from high bias, or error resulting from inaccuracies in its hypothesis class, because its hypothesis function is so simple it cannot accurately represent many complex situations.
What kind of smoothing is used in Laplace smoothing?
Laplace smoothing consists of making the prior assumption that we have observed each possible value of each feature once in the past (before seeing the training data set).
What is the decision boundary for naive Bayes?
Naive Bayes Classifier is based upon two important principles: the assumption that all features are independent (the “Naive” part of the classifier) and Bayes’ theorem. P ( A | B) refers to the probability that A will be true given that B is true.