Calculate predictive power score for x on y
score(
df,
x,
y,
algorithm = "tree",
metrics = list(regression = "MAE", classification = "F1_weighted"),
cv_folds = 5,
seed = 1,
verbose = TRUE
)
a named list, potentially containing
the name of the predictor variable
the name of the target variable
text showing how to interpret the resulting score
the predictive power score
the evaluation metric used to compute the PPS
the score of a naive model on the evaluation metric
the score of the predictive model on the evaluation metric
how many cross-validation folds were used
the seed that was set
text shwoing what algorithm was used
text showing whether classification or regression was used
data.frame containing columns for x and y
string, column name of predictor variable
string, column name of target variable
string, see available_algorithms()
named list of eval_*
functions used for
regression and classification problems, see available_evaluation_metrics()
float, number of cross-validation folds
float, seed to ensure reproducibility/stability
boolean, whether to print notifications
score(iris, x = 'Petal.Length', y = 'Species')
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