Regression data to predict the number of runs scored. Obtained using the mlr3oml package.
Contains 14 features and 1232 observations.
Target column is "rs".
Pre-processing
All variable names have been converted from upper case to lower case.
The variables
"year","rs","ra","w"` have been coerced to integers.
Examples
data("moneyball", package = "mlr3data")
str(moneyball)
#> 'data.frame': 1232 obs. of 15 variables:
#> $ team : Factor w/ 39 levels "ARI","ATL","BAL",..: 1 2 3 4 5 6 7 8 9 10 ...
#> $ league : Factor w/ 2 levels "AL","NL": 2 2 1 1 2 1 2 1 2 1 ...
#> $ year : int 2012 2012 2012 2012 2012 2012 2012 2012 2012 2012 ...
#> $ rs : int 734 700 712 734 613 748 669 667 758 726 ...
#> $ ra : int 688 600 705 806 759 676 588 845 890 670 ...
#> $ w : int 81 94 93 69 61 85 97 68 64 88 ...
#> $ obp : num 0.328 0.32 0.311 0.315 0.302 0.318 0.315 0.324 0.33 0.335 ...
#> $ slg : num 0.418 0.389 0.417 0.415 0.378 0.422 0.411 0.381 0.436 0.422 ...
#> $ ba : num 0.259 0.247 0.247 0.26 0.24 0.255 0.251 0.251 0.274 0.268 ...
#> $ playoffs : Factor w/ 2 levels "0","1": 1 2 2 1 1 1 2 1 1 2 ...
#> $ rankseason : Factor w/ 8 levels "1","2","3","4",..: NA 4 5 NA NA NA 2 NA NA 6 ...
#> $ rankplayoffs: Factor w/ 5 levels "1","2","3","4",..: NA 5 4 NA NA NA 4 NA NA 2 ...
#> $ g : Factor w/ 8 levels "158","159","160",..: 5 5 5 5 5 5 5 5 5 5 ...
#> $ oobp : num 0.317 0.306 0.315 0.331 0.335 0.319 0.305 0.336 0.357 0.314 ...
#> $ oslg : num 0.415 0.378 0.403 0.428 0.424 0.405 0.39 0.43 0.47 0.402 ...