msparvika / logistic-regression-vs-naive-bayes Goto Github PK

It compares two approaches : Naive Bayes and Logistic Regression -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.

-In statistics, the logistic model (or logit model) is a widely used statistical model that, in its basic form, uses a logistic function to model a binary dependent variable; many more complex extensions exist. In regression analysis, logistic regression (or logit regression) is estimating the parameters of a logistic model; it is a form of binomial regression.

All the methods are implemented from scratch except basic functions. The data is random which has 4 Attributes and one label.

Libraries needed : csv, numpy ,random, math, KFold