Image Classification On Fashion MNIST dataset using Logistic Regression

Logistic regression is a statistical method for predicting binary classes. It is an extension of the linear regression for the classification problem approaches.It is named logistic because the function used in the logistic regression is logistic function also known as sigmoid function. This function is developed by statisticians to describe properties of population growth in ecology, rising quickly. Difference between Linear Regression and Logistic regression Linear regression model can work for regression but fails for classification. A linear model does not output probabilities, but it treats the classes are numbers (0 and 1) and fits the best hyperplane that minimizes the distances between with this approach. Linear regression is best for predicting the value on the scale 0–100. Linear regression predictions are continuous while in Logistic regression helps in prediction of the data that is in binary form. <a href="https://medium.com/@techtangent.in/image-classification-on-fashion-mnist-dataset-using-logistic-regression-c079fec56712">Website</a>