Ordinary Least Squares

Ordinary Least Squares or Linear Least Squares is the simplest algorithm for linear regression.

The {{ isCausalPrediction() | targetRoleName}} variable is computed as the sum of weighted input variables. OLS finds the appropriate weights by minimizing the cost function (i.e., how "wrong" the algorithm is).
OLS is very simple and provides a very "explainable" model, but:

it cannot automatically fit data for which the {{ isCausalPrediction() | targetRoleName}} variable is not the result of a linear combination of input features
it is highly sensitive to errors in the input dataset and prone to overfitting

Parallelism

Number of cores used for parallel training. Using more cores leads to faster training but at the expense of more memory consumption, especially for large training datasets.