Performance of Regression Models
Compute indices of model performance for regression models.
## S3 method for class 'lm' model_performance(model, metrics = "all", verbose = TRUE, ...)
model |
A model. |
metrics |
Can be |
verbose |
Toggle off warnings. |
... |
Arguments passed to or from other methods. |
Depending on model
, following indices are computed:
AIC Akaike's Information Criterion, see ?stats::AIC
AICc Second-order (or small sample) AIC with a correction for small sample sizes
BIC Bayesian Information Criterion, see ?stats::BIC
R2 r-squared value, see r2
R2_adj adjusted r-squared, see r2
RMSE root mean squared error, see performance_rmse
SIGMA residual standard deviation, see get_sigma()
LOGLOSS Log-loss, see performance_logloss
SCORE_LOG score of logarithmic proper scoring rule, see performance_score
SCORE_SPHERICAL score of spherical proper scoring rule, see performance_score
PCP percentage of correct predictions, see performance_pcp
A data frame (with one row) and one column per "index" (see metrics
).
model <- lm(mpg ~ wt + cyl, data = mtcars) model_performance(model) model <- glm(vs ~ wt + mpg, data = mtcars, family = "binomial") model_performance(model)
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