linear_regression

#Linear Regression

##Description Linear Regression is a simple regression algorithm that can map an N-dimensional signal to a 1-dimensional signal.

The Linear Regression algorithm is a supervised learning algorithm that can be used for regression for any type of N-dimensional signal.

The Linear Regression algorithm is part of the GRT regression modules.

##Advantages The Linear Regression algorithm is a simple regression algorithm that can map an N-dimensional signal to a 1-dimensional signal. It works well if your data has a clear linear trend.

##Disadvantages The main limitation of the Linear Regression algorithm is that the mapping needs to be linear. Linear Regression can only map an N-dimensional signal to a 1-dimension signal. If you need a regression algorithm that can map an N-dimensional to an M-dimensional signal, or you need a regression algorithm that can perform non-linear mapping then you should try the MLP regression algorithm or the MultidimensionalRegression algorithm instead.

##Things To Know You should always enable scaling with Linear Regression, as this will give you much better results.

##Training Data Format You should use the RegressionData data structure to train the Linear Regression algorithm.

##Example Code LinearRegression Example