Regression and Classification | Supervised Machine Learning. Removing stop words with NLTK in Python.Decision Tree Introduction with example.Note: In this article, we refer to dependent variables as responses and independent variables as features for simplicity. In order to provide a basic understanding of linear regression, we start with the most basic version of linear regression, i.e. Simple linear regression is an approach for predicting a response using a single feature. It is assumed that the two variables are linearly related. Let us consider a dataset where we have a value of response y for every feature x: 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). Y as response vector, i.e y = įor n observations (in above example, n=10).Ī scatter plot of the above dataset looks like. (i.e a value of x not present in a dataset) Now, the task is to find a line that fits best in the above scatter plot so that we can predict the response for any new feature values. H(x_i) represents the predicted response value for i th observation.The equation of regression line is represented as: b_0 and b_1 are regression coefficients and represent y-intercept and slope of regression line respectively.To create our model, we must “learn” or estimate the values of regression coefficients b_0 and b_1. And once we’ve estimated these coefficients, we can use the model to predict responses! In this article, we are going to use the principle of Least Squares. Here, e_i is a residual error in ith observation. So, our aim is to minimize the total residual error.
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