How is the naive Bayes classifier used in text classification?
The Naive Bayes classifier is a simple classifier that classifies based on probabilities of events. It is the applied commonly to text classification. Though it is a simple algorithm, it performs well in many text classification problems.
Which is more accurate, naive Bayes or SVM?
We achieve an accuracy score of 78% which is 4% higher than Naive Bayes and 1% lower than SVM. As you can see, following some very basic steps and using a simple linear model, we were able to reach as high as an 79% accuracy on this multi-class text classification data set.
Which is better for classifying NB or SVM?
While (Ng and Jordan, 2002) showed that NB is better than SVM/logistic regression (LR) with few training cases, MNB is also better with short documents. SVM usually beats NB when it has more than 30–50 training cases, we show that MNB is still better on snippets even with relatively large training sets (9k cases).
Which is better support vector machine or Bayes?
Support Vector Machine (SVM) is better at full-length content. Multinomial Naive Bayes (MNB) is better at snippets. MNB is stronger for snippets than for longer documents.
What is the probability of a fail in naive Bayes?
P (Fail | Poor) = 0.66 * (6/13) / (5/13) = 0.66 * 6 / 5 = 0.792 This is a higher probability. We use a similar method in Naive Bayes to give the probability of different class and then label it with the class having maximum probability.
How to label a fruit in naive Bayes?
We use a similar method in Naive Bayes to give the probability of different class and then label it with the class having maximum probability. Let’s take an example, where we want to tell if a fruit is tomato or not. We can tell it’s a tomato from it’s shape, color and diameter (size). Tomato is red, it’s round and has about 9-10 cm diameter.
How is Bayes theorem used in text classification?
It works on the famous Bayes theorem which helps us to find the conditional probabilities of occurrence of two events based on the probabilities of occurrence of each individual event. their results (Pass and Fail). Student will fail if his efforts are poor.