Description Usage Arguments Details Value See Also Examples

Interpret a model via regularized coefficient estimates

1 |

`x` |
a model_list object containing a glmnet model |

`sparsity` |
If NULL (default) coefficients for the best-performing model will be returned. Otherwise, a value in [0, 1] that determines the sparseness of the model for which coefficients will be returned, with 0 being maximally sparse (i.e. having the fewest non-zero coefficients) and 1 being minimally sparse |

`remove_zeros` |
Remove features with coefficients equal to 0? Default is TRUE |

`top_n` |
Integer: How many coefficients to return? The largest top_n absolute-value coefficients will be returned. If missing (default), all coefficients are returned |

**WARNING** Coefficients are on the scale of the predictors; they
are not standardized, so unless features were scaled before training (e.g.
with `prep_data(..., scale = TRUE)`

, the magnitude of coefficients
does not necessarily reflect their importance.

If x was trained with more than one value of alpha the best value of alpha is used; sparsity is determined only via the selection of lambda. Using only lasso regression (i.e. alpha = 1) will produce a sparser set of coefficients and can be obtained by not tuning hyperparameters.

A data frame of variables and their regularized regression coefficient estimates with parent class "interpret"

1 2 3 4 5 | ```
m <- machine_learn(pima_diabetes, patient_id, outcome = diabetes, models = "glm")
interpret(m)
interpret(m, .2)
interpret(m) %>%
plot()
``` |

healthcareai documentation built on Sept. 2, 2018, 1:03 a.m.

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