The line which best fits is called the Regression line. Now, we have to find a line that fits the above scatter plot through which we can predict any value of y or response for any value of x for n observations (in above example, n=10).Hence, we try to find a linear function that predicts the response value(y) as accurately as possible as a function of the feature or independent variable(x).įor understanding the concept let’s consider a salary dataset where it is given the value of the dependent variable(salary) for every independent variable(years experienced). It is assumed that the two variables are linearly related. One variable denoted x is regarded as an independent variable and the other one denoted y is regarded as a dependent variable. It is a statistical method that allows us to summarize and study relationships between two continuous (quantitative) variables. Let’s discuss Simple Linear regression using R. ![]() There are two types of linear regression. Decision Tree Introduction with example.Removing stop words with NLTK in Python.Regression and Classification | Supervised Machine Learning.Basic Concept of Classification (Data Mining).Gradient Descent algorithm and its variants.ML | Momentum-based Gradient Optimizer introduction.Optimization techniques for Gradient Descent.ML | Mini-Batch Gradient Descent with Python.Difference between Batch Gradient Descent and Stochastic Gradient Descent. ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |