Reading Time: < 1 minute Hi all, Knoldus has organized a 30 min session on 27th April 2018 at 4:00 PM. The topic was NAIVE BAYES CLASSIFIER. Many people have joined and enjoyed the session. I am going to share the slides here. Please let me know if you have any question related to linked slides.
Reading Time: 4 minutes I hope we understand the conditional probabilities and Bayes theorem through our previous blog. Now let’s use this understanding to find out more about the naive Bayes classifier. NAIVE BAYES CLASSIFIER Naive Bayes is a simple technique for constructing classifiers: models that assign class labels to problem instances, represented as vectors of feature values, where the class labels are drawn from some finite set. Naive Bayes Continue Reading
Reading Time: 4 minutes In machine learning, Naive Bayes classifiers are a family of simple “probabilistic classifiers “based on applying Bayes’ theorem with strong (naive) independence assumptions between the features. The Naive Bayes Classifier technique is based on the so-called Bayesian theorem and is particularly suited when the dimensionality of the inputs is high. Despite its simplicity, Naive Bayes can often outperform more sophisticated classification methods. In simple terms, a Naive Bayes classifier assumes that Continue Reading