machine learning Linear vs Logistic Regression We have explored the differences between Linear and Logistic regression in depth. We looking into the applications of Linear and Logistic regression along with a basic background.

machine learning Data Analysis using Regression In this article, we have clarified the most usually utilized kinds of regressions in information science and its application in data analysis

regression Logistic Regression Logistic Regression is an efficient regression algorithm that aims to predict categorical values, often binary. It is widely used in the medical field to classify sick and healthy individuals and areas that need to determine a client's risk such as financial companies.

machine learning Principal Component Regression (PCR) Principal Component Regression (PCR) is an algorithm for reducing the multi-collinearity of a dataset. PCR is basically using PCA, and then performing Linear Regression on these new PCs. The key idea of how PCR aims to do this, is to use PCA on the dataset before regression.

machine learning Ridge Regression Ridge regression is an efficient regression technique that is used when we have multicollinearity or when the number of predictor variables in a set exceed the number of observations. It uses L2 regularization and solves the problem of overfitting. Concepts of overfitting and regularization is basis

machine learning Summary of Regression Techniques Regression is a technique based on statistics to model the relationship between a set of variables to make predictions on unseen data. We explored are Linear, Logistic, Polynomial, Ridge, Lasso, Elastic Net, Stepwise regression.

Artificial Intelligence Decision Trees Decision Tree is a popular machine learning algorithm mainly used for classification. Concepts of entropy and information gain are required to apply decision tree for a data set. It is used for non-linear classification and regression Learn through an example