Naive Bayes is a machine learning implementation of Bayes Theorem. Comparison of Naive Bayes and Decision Tree on Feature Selection Using Genetic Algorithm for Classification Problem To cite this article: S Rahmadani et al 2018 J. A key difference between the two models, is that … Is there any fundamental difference ? Decision tree vs naive Bayes : Decision tree is a discriminative model, whereas Naive bayes is a generative model. Which ever performs best will more likely perform better in the field. Phys. Some examples might make this clearer: Naive Bayes and K-NN, are both examples of supervised learning (where the data comes already labeled). Decision tree is faster due to KNN’s expensive real time execution. First of all let's start off by saying they're both classifiers, meant to solve a problem called statistical classification.This means that you have lots of data (in your case articles) split into two or more categories (in your case positive/negative sentiment). It is a classification algorithm that predicts the probability of each data point belonging to a class and then classifies the point as the class with the highest probability. 978 012087 View the article online for updates and enhancements. Digital Analytics Decision Trees ; CHAID vs CART ... are cut off and the tree stops at that branch. Some algorithm like naive bayes and Decision tree works on labeled data where you have a classification column. CART, C5.0, C4.5 and so forth can lead to nice rules. Decision tree is useful to obtain a proper set of rules from a large amount of instances. Note that the Naive Bayes algorithm stated otherwise. For example if you want to relate the weather status and day of week with the punctuality of train then it should be labeled data. On the difference between Naive Bayes and Recurrent Neural Networks. Both the random forest and decision trees are a type of classification algorithm, which are supervised in nature. However, it has difficulty in obtaining the relationship between continuous-valued data points. So What is a decision tree? 1) In terms of decision trees, the comprehensibility will depend on the tree type. Decision trees are easy to use for small amounts of classes.

K-means, decision trees, and Naive Bayes are just a few of the algorithms used in marketing. Naïve Bayes has a naive assumption of conditional independence for every feature, which means that the algorithm expects the features to be independent which not always is the case. Decision trees are more flexible and easy. : Conf.

Decision Tree and Naïve Bayes Algorithm fo r Classification and Genera tion of Actionable Knowledge for Direct Marketing 199 Table 1. The model is the “thing” that is saved after running a machine learning algorithm on training data and represents the rules, numbers, and any other algorithm-specific data structures required to make predictions.