Purnananda Behera

What is Logistic Regression?

• Logistic regression is a technique used for solving the classification problem.
• Classification is technique to categorize our data into a desired and distinct number of classes where we can assign label to each class.
• And Classification is nothing but a problem of identifying to which of a set of categories a new observation belongs, on the basis of training dataset containing observations (or instances) whose categorical membership is known.
• For example to predict:
  • Whether an email is spam (1) or not (0) or,
  • Whether the tumor is malignant (1) or not (0)

Below is the pictorial representation of a basic logistic regression model to classify set of images into happy or sad.

Both Linear regression and Logistic regression are supervised learning techinques. But for the Regression problem the output is continuous unlike the classification problem where the output is discrete.

• Logistic Regression is used when the dependent variable(target) is categorical.
  
• Sigmoid function or logistic function is used as hypothesis function for logistic regression. Below is a figure showing the difference between linear regression and logistic regression, Also notice that logistic regression produces a logistic curve, which is limited to values between 0 and 1.

To be continued..