score: Calculate predictive power score for x on y

In ppsr: Predictive Power Score

Description

Calculate predictive power score for x on y

Usage

score(
  df,
  x,
  y,
  algorithm = "tree",
  metrics = list(regression = "MAE", classification = "F1_weighted"),
  cv_folds = 5,
  seed = 1,
  verbose = TRUE
)

Arguments

df	data.frame containing columns for x and y
x	string, column name of predictor variable
y	string, column name of target variable
algorithm	string, see available_algorithms()
metrics	named list of eval_* functions used for regression and classification problems, see available_evaluation_metrics()
cv_folds	float, number of cross-validation folds
seed	float, seed to ensure reproducibility/stability
verbose	boolean, whether to print notifications

Value

a named list, potentially containing

x

the name of the predictor variable

y

the name of the target variable

result_type

text showing how to interpret the resulting score

pps

the predictive power score

metric

the evaluation metric used to compute the PPS

baseline_score

the score of a naive model on the evaluation metric

model_score

the score of the predictive model on the evaluation metric

cv_folds

how many cross-validation folds were used

seed

the seed that was set

algorithm

text shwoing what algorithm was used

model_type

text showing whether classification or regression was used

Examples

score(iris, x = 'Petal.Length', y = 'Species')

ppsr documentation built on March 2, 2021, 5:06 p.m.