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stats::lm() uses ordinary least squares to fit models with numeric outcomes.


For this engine, there is a single mode: regression

Tuning Parameters

This engine has no tuning parameters.

Translation from parsnip to the original package

## Linear Regression Model Specification (regression)
## Computational engine: lm 
## Model fit template:
## stats::lm(formula = missing_arg(), data = missing_arg(), weights = missing_arg())

Preprocessing requirements

Factor/categorical predictors need to be converted to numeric values (e.g., dummy or indicator variables) for this engine. When using the formula method via fit(), parsnip will convert factor columns to indicators.

Case weights

This model can utilize case weights during model fitting. To use them, see the documentation in case_weights and the examples on

The fit() and fit_xy() arguments have arguments called case_weights that expect vectors of case weights.

However, the documentation in stats::lm() assumes that is specific type of case weights are being used: “Non-NULL weights can be used to indicate that different observations have different variances (with the values in weights being inversely proportional to the variances); or equivalently, when the elements of weights are positive integers w_i, that each response y_i is the mean of w_i unit-weight observations (including the case that there are w_i observations equal to y_i and the data have been summarized). However, in the latter case, notice that within-group variation is not used. Therefore, the sigma estimate and residual degrees of freedom may be suboptimal; in the case of replication weights, even wrong. Hence, standard errors and analysis of variance tables should be treated with care” (emphasis added)

Depending on your application, the degrees of freedown for the model (and other statistics) might be incorrect.

Saving fitted model objects

This model object contains data that are not required to make predictions. When saving the model for the purpose of prediction, the size of the saved object might be substantially reduced by using functions from the butcher package.


The “Fitting and Predicting with parsnip” article contains examples for linear_reg() with the "lm" engine.


  • Kuhn, M, and K Johnson. 2013. Applied Predictive Modeling. Springer.