maiotracking.blogg.se - The simple linear regression equation keyboard

Standardize predictor variables: Select to make all variables the same size based on the algorithm used.Enter value of alpha: Select a value between 0 (ridge regression) and 1 (lasso) to measure the amount of emphasis given to the coefficient.Use regularized regression: Select to balance the same minimization of sum of squared errors with a penalty term on the size of the coefficients and produce a simpler model.Use a weight variable for weighted least squares: Select a variable to determine the amount of importance to place on each record when creating a least-squares model.Omit a model constant: Select to omit a constant and have the best fit line pass through the origin.Select Customize to modify the Model, Cross-validation, and Plots settings. They have no predictive value and can cause runtime exceptions. Columns containing unique identifiers, such as surrogate primary keys and natural primary keys, should not be used in statistical analyses. Any number of predictor variables can be selected, but the target variable should not also be a predictor variable.

A predictor variable is also known as a feature or an independent variable.

Select the predictor variables: Select the data to use to influence the value of the target variable.

A target variable is also known as a response or dependent variable.

Select the target variable: Select the data to be predicted.

No other special characters are allowed, and R is case sensitive. Model names must start with a letter and may contain letters, numbers, and the special characters period (.) and underscore (_).

Model name: Enter a name for the model to identify the model when it is referenced in other tools.

The advantage of using the RevoScaleR based function is that it allows much larger (out of memory) datasets to be analyzed, but at the cost of additional overhead to create an XDF file and the inability to create some of the model diagnostic output that is available with the open source R functions.

If the input data comes from either an XDF Output tool or XDF Input tool, then the RevoScaleR rxLinMod function is used for model estimation. If the input data is from an Alteryx data stream, then the open-source R lm function and the glmnet and cv.glmnet functions (from the glmnet package) is used for model estimation.