Learn how to use the Naive Bayes Classifier for fast and accurate classification in your machine learning projects. Start Reading Now!
Naive Bayes classifiers, a family of classifiers that are based on the popular Bayes’ probability theorem, are known for creating simple yet well performing models, especially in the fields of document classification and disease prediction. In this first part of a series, we will take a look a...
“hidden variables” which are believed to form a relationship. For example, in the case of medical data, a hidden variable may indicate a syndrome, representing a number of symptoms that could characterise a disease (Han et al., 2011). Bayesian Belief Networks are different to naiveBayes ...
Question: Use the Naive Bayes Classifier to implement a spam filter that learns word spam probabilities from our prelabeled training data and then predicts the label (ham or spam) of a set of emails that it hasn’t seen before. Basically in th...
Step-wise approach to data analysis. Contribute to aayush26/Data-Analysis development by creating an account on GitHub.
The objective function in the naive Bayes probability is to maximize the posterior probability given the training data in order to formulate the decision rule. To continue with our example above, we can formulate the decision rule based on the posterior probabilities as follows: ...