What algorithm is used for continuous and categorical target variable?

So you can use it directly in SVM, neural networks, or any other model which takes continuous inputs. Originally Answered: How do I perform a KNN algorithm with a mix of Categorical and Continuous Variables? The intuition of the KNN algorithm is that, the closer the points in space, the more similar they are.

Which algorithm is suited for the categorical data?

Logistic Regression is a classification algorithm so it is best applied to categorical data.

Which is best for categorical variables?

In order to understand categorical variables, it is better to start with defining continuous variables first. Continuous variables can take any number of values. A good example of the continuous variable is weight or height. They both can take theoretically any value.

Which algorithm is used for the prediction of continuous variables?

Linear Regression
1) Linear Regression It is one of the most-used regression algorithms in Machine Learning. A significant variable from the data set is chosen to predict the output variables (future values). Linear regression algorithm is used if the labels are continuous, like the number of flights daily from an airport, etc.

Which algorithm is used for predicting continuous dependent variables?

Regression analysis is used when you want to predict a continuous dependent variable from a number of independent variables. If the dependent variable is dichotomous, then logistic regression should be used.

What is a continuous and categorical variable?

Categorical variables contain a finite number of categories or distinct groups. Continuous variables are numeric variables that have an infinite number of values between any two values. A continuous variable can be numeric or date/time. For example, the length of a part or the date and time a payment is received.

Which machine learning algorithms is best for continuous data?

1) Linear Regression It is one of the most-used regression algorithms in Machine Learning. A significant variable from the data set is chosen to predict the output variables (future values). Linear regression algorithm is used if the labels are continuous, like the number of flights daily from an airport, etc.

What are continuous and categorical variables?

What is categorical variable in machine learning?

A categorical variable is a variable whose values take on the value of labels. Machine learning algorithms and deep learning neural networks require that input and output variables are numbers. This means that categorical data must be encoded to numbers before we can use it to fit and evaluate a model.