In this Article, will learn about logistic regression from scratch using python. I will explain stepwise the details of how logistic regression works and then we will code using the IRIS dataset.
We will try to Learn logistic Regression from the neural network perspective. In simple terms logistic regression can be described as y = w*x +b where y is the dependent variable, x is the independent variable, w is weights of the equations or model and b is constant or bias of the model.
Unlike all regression methods, logistic regression is used for classification as it outputs(0 and 1) binary labels. In the linear regression method, we try to find the best trendline which fits the data well whereas in logistic regression we try to learn the best combinations of weights to classify the input correctly.