Linear Regression is a supervised machine learning algorithm where the predicted output is continuous and has a constant slope. It’s used to predict values within a continuous range, (e.g. sales, price) rather than trying to classify them into categories (e.g. cat, dog).
**Polynomial Regression:** In this technique, a curve fits into the data points. In a polynomial regression equation, the power of the independent variable is greater than 1. Although higher degree polynomials give lower error, they might also result in over-fitting.**Regularization:** is a technique used to solve the overfitting problem in statistical models. In machine learning, regularization penalizes the coefficients such that the model generalize better. We have different types of regression techniques which uses regularization such as Ridge regression and lasso regression. Ridge regression performs**Ridge Regression:** , i.e. it adds a factor of sum of squares of coefficients in the optimization objective. Thus, ridge regression optimizes the**L2 regularization** Lasso, or Least Absolute Shrinkage and Selection Operator, is quite similar conceptually to ridge regression. It also adds a penalty for non-zero coefficients, but unlike ridge regression which penalizes sum of squared coefficients(the so-called L2 penalty), lasso penalizes the sum of their absolute values (L1 penalty). As a result, for high values of Lambda, many coefficients are exactly zeroed under lasso, which is never the case in ridge regression. is a supervised learning classification algorithm used to predict the probability of a target variable. The nature of target or dependent variable is dichotomous, which means there would be only two possible classes.In simple words, the dependent variable is binary in nature having data coded as either 1 (stands for success/yes) or 0 (stands for failure/no).**Lasso Regression:** This regression uses Sigmoid Function to calculate the Probabilities**Logistic regression**
Originally published at https://www.jayvinay.com on September 2, 2020.
