The phrase "as a regression for" is correct and usable in written English.
It can be used in statistical or analytical contexts where you are discussing a model or method that serves as a regression analysis for a particular variable or outcome.
Example: "The new model was developed as a regression for predicting sales based on historical data."
Alternatives: "as a model for" or "as an analysis for".
Prediction/Decision trees can perform classification for predicting what group a case belongs to, as well as regression for predicting a specific value.
Classification and regression tree is a statistical method for exploring and constructing a nonlinear model, by a decision tree, which can be expressed as a regression tree for a continuous response variable or a classification tree for a categorical variable.
Originally proposed as a regression hypothesis for the study of memory disorders, specifically retrograde amnesia, by Ribot (1881), it has been extended to the study of brain aging and pathology and to language.
SVM has been used widely as a regression algorithm for both linear and nonlinear modeling but this approach neglects that the filter is derived by errors which may lead to a growth of the overall system errors.
A quadratic polynomial in days on test was used as a regression function for weekly means of daily feed intake and to escribe its residual variance.
The strength of association between predictor and outcome is reported as a regression coefficient for change in the log of counts when the factor is present, and can be exponentiated as an incident rate ratio with 95% confidence interval (IRR, CI95).
A continuous U tree algorithm transfers traditional U tree algorithm to reinforcement learning and this U tree algorithm can be viewed as a Regression Tree algorithm for storing state values [154].
The approach considered in this study is to treat the sparse representation equation as a regression-based problem, for which applicable techniques include least squares (LS) and ℓ 1 and ℓ 2 norm minimization [37,39,40].
The pseudo-values are then used as responses in a regression model for longitudinal data, where time is a covariate.
Sentiment analysis has also been used as a factor in a regression model for predicting box-office revenues for movies [2].
