Data World Blog

This algorith is based on probabilty, the probability captures the chance that an event will occur in the light of the available evidence. The lower the probability, the less likely the event is to occur. A probability of 0 indicates that the event will definitily not occur, while a probability of 1 indicates that the event will occur with 100 percent certainty. This classifier uses training data to calculate an observed probability of each outcome based on the evidence provided bu feature values. When the classifier is later applied to unlabeled data, it uses the observed probabilities to predict the most likely class for the new feature. This classifier has been used for: Text classification . Intrusion detection in computer networks . Diagnosing medical conditions The relationship between dependent events can be described using Bayes’ theorem, which provides a way of thinking about how to revise an estimate of probabilities of one event in light of the evidence provided by an...

Decision tree learners are powerful classifiers, which utilizes a tree structure to model the relationship among the features and the potential outcomes. The tree has a  root node  and  decision nodes  where choices are made. The choices split the data across branches that indicate the potential outcomes of a decision. The tree is terminated by  leaf nodes  (or terminal nodes) that denote the action to be taken as the result of the series of the decisions. In the case of a predictive model, the leaf nodes provide the expected result given the series of events in the tree. After the model is created, many decision trees algorithms output the resulting structure in a human-readable format. This provides tremendous insight into how and why the model works or doesn’t work well for a particular task. This also makes decision trees particularly appropriate for applications in which the classification mechanism needs to be transparent for legal reasons, or in c...