Why we use logistic regression over linear regression?

Linear Regression is used to handle regression problems whereas Logistic regression is used to handle the classification problems. Linear regression provides a continuous output but Logistic regression provides discreet output.

Why we use logistic regression in machine learning?

Logistic regression is one of the most popular Machine Learning algorithms, which comes under the Supervised Learning technique. It is used for predicting the categorical dependent variable using a given set of independent variables. Logistic regression predicts the output of a categorical dependent variable.

What is a good use case for logistic regression?

Logistic regression is used when the outcome is a discrete variable. Example, trying to figure out who will win the election, whether a student will pass or fail an exam, whether a customer will come back, whether an email is a spam.

How does logistic regression works in machine learning?

Logistic regression is a supervised learning classification algorithm used to predict the probability of a target variable. The nature of target or dependent variable is dichotomous, which means there would be only two possible classes. Mathematically, a logistic regression model predicts P(Y=1) as a function of X.

How does logistic regression work in machine learning?

What is logistic regression and linear regression?

Linear regression is used for predicting the continuous dependent variable using a given set of independent features whereas Logistic Regression is used to predict the categorical. Linear regression is used to solve regression problems whereas logistic regression is used to solve classification problems.

What is logistic regression machine learning?

Logistic regression is a supervised learning classification algorithm used to predict the probability of a target variable. It is one of the simplest ML algorithms that can be used for various classification problems such as spam detection, Diabetes prediction, cancer detection etc.