Numerical prediction (Regression) Description and Preparation

Description of numerical prediction (Regression)

This functions is predicting a numerical value based on known data. It is also called regression.

The objective variable that you want to predict is called the "objective variable" and the variable that factors in the objective variable is called the "explanatory variable".

Regression uses opportunity learning to create a predictive model from a known data set with a set of objective and explanatory variable values, and then assigns unknown explanatory variable values to the model to predict the unknown objective variable values.

Data Preparation

To perform regression, you need to prepare two sets of data: a "training data set" of known data with objective and explanatory variables, and "prediction data" with the explanatory variable values you wish to predict.

※Data for training and forecasting can be analyzed in one csv file or in separate files.

Gofard allows you to input data in the order of the numbers in the sidebar, run calculations to validate the forecast model, and output a csv file containing the forecast values.