What is the application of logistic regression?

It is used in statistical software to understand the relationship between the dependent variable and one or more independent variables by estimating probabilities using a logistic regression equation. This type of analysis can help you predict the likelihood of an event happening or a choice being made.

What is logistic regression algorithm in Machine Learning?

Logistic Regression is a Machine Learning algorithm which is used for the classification problems, it is a predictive analysis algorithm and based on the concept of probability. The hypothesis of logistic regression tends it to limit the cost function between 0 and 1 .

What are regression algorithms used for?

Regression algorithms predict the output values based on input features from the data fed in the system. The go-to methodology is the algorithm builds a model on the features of training data and using the model to predict the value for new data.

What is Logistic regression how Logistic regression is useful for classification problems?

What Is Logistic Regression? Logistic regression is a classification algorithm, used when the value of the target variable is categorical in nature. Logistic regression is most commonly used when the data in question has binary output, so when it belongs to one class or another, or is either a 0 or 1.

Is Logistic regression is mainly used for regression?

It can be used for Classification as well as for Regression problems, but mainly used for Classification problems. Logistic regression is used to predict the categorical dependent variable with the help of independent variables. The output of Logistic Regression problem can be only between the 0 and 1.

What is regression in supervised machine learning?

Regression is a supervised machine learning technique which is used to predict continuous values. The ultimate goal of the regression algorithm is to plot a best-fit line or a curve between the data. Polynomial regression is used when the data is non-linear.

Is logistic regression is mainly used for regression?