# Which algorithm predicts continuous value?

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A classification algorithm may predict a continuous value, but the continuous value is in the form of a probability for a class label. A regression algorithm may predict a discrete value, but the discrete value in the form of an integer quantity.

## How do you predict continuous values?

When you are trying to predict continuous variable with N number of inputs it become a classic example of Regression Problem.

1. Linear Regression.
2. Logistic Regression.
3. Polynomial Regression.
4. Stepwise Regression.
5. Ridge Regression.
6. Lasso Regression.
7. ElasticNet Regression,

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

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 types of algorithms are used when the target variables has continuous values?

1. Linear Regression. Linear regression is a supervised learning algorithm and tries to model the relationship between a continuous target variable and one or more independent variables by fitting a linear equation to the data.

## Which model works on continuous data?

A continuous response variable can be modeled using ordinary least-squares regression (OLS regression), one of the GLM modeling techniques.

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## Can neural network predict continuous value?

To predict a continuous value, you need to adjust your model (regardless whether it is Recurrent or Not) to the following conditions: Use a linear activation function for the final layer. Chose an appropriate cost function (square error loss is typically used to measure the error of predicting real values)

## What is the best algorithm for prediction?

Top Machine Learning Algorithms You Should Know

• Linear Regression.
• Logistic Regression.
• Linear Discriminant Analysis.
• Classification and Regression Trees.
• Naive Bayes.
• K-Nearest Neighbors (KNN)
• Learning Vector Quantization (LVQ)
• Support Vector Machines (SVM)

## What is difference between correlation and regression?

Correlation is a statistical measure that determines the association or co-relationship between two variables. Regression describes how to numerically relate an independent variable to the dependent variable. … Regression indicates the impact of a change of unit on the estimated variable ( y) in the known variable (x).