What type of algorithm is suitable for predicting the continuous dependent variable?

What type of algorithm is suitable for predicting the continuous dependent variable?

Linear regression is to be used when the target variable is continuous and the dependent variable(s) is continuous or a mixture of continuous and categorical, and the relationship between the independent variable and dependent variables are linear.

What type of Machine Learning algorithm is suitable for predicting the continuous dependent variable with two different values?

Linear regression is usually among the first few topics which people pick while learning predictive modeling. In this technique, the dependent variable is continuous, independent variable(s) can be continuous or discrete, and nature of regression line is linear.

Which is algorithms are used to predict continuous values?

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.

What is the best regression model to predict a continuous variable?

If the response is a measurements where positive and negative deviations from the expected value are equally likely, then you can use a (standard) linear model. It might also be possible that you should transform the response or to use a generalized linear model of the Gamma-family.

Can a continuous variable be used as an independent variable?

It sounds like your independent variable is actually a categorical variable with two levels. Continuous variables don’t have levels like that. You can perform simple regression with one categorical independent variable. However, for simplicity, you can perform a 2-sample t-test and assess the mean difference between your two groups.

Can a binary dependent variable be used in regression?

Yes you can! In your case, you’re talking about a binary dependent variable because it has only two levels (presumably), admitted and not admitted. In that case, you’d use binary logistic regression and it’s fine to use a binary (or categorical) independent variable.