```
#'
#' Prediction Scoring
#'
#' @param preds Predicted Values
#' @param obs Observed or Actual Values
#' @param numVar Number of predictors used to build the model.
#' If NULL, general R squared is calculated with no AIC and BIC.
#'
#' @return Prediction scores
#'
#' @examples
#' predScores(c(1,1,1),c(1,0.5,1))
#'
predScores <- function(preds, obs, numVar = NULL){
# Need to add different behaviour for classification and regression
cat("Predicted Values : ",NROW(preds),"has Mean:", mean(preds)," SD:",sd(preds),"\n")
cat("Actual Values : ",NROW(obs),"has Mean:", mean(obs)," SD:",sd(obs),"\n")
cat("Number of Predictors: ",numVar,"\n")
cat("Chi square test:\n")
print(chisq.test(x = obs,y = preds))
cat("Scores:\n")
cat("\tRMSE :",rmse(preds,obs),"\n")
cat("\tMAE :",mae(preds,obs),"\n")
cat("\tMAPE :",mape(preds,obs),"\n")
cat("\tRsquared :",rsquared(preds,obs),"\n")
cat("\tAdjusted Rsquared :",rsquared(preds,obs),"\n")
cat("\tAIC :",aic(preds,obs,numVar),"\n")
cat("\tBIC :",bic(preds,obs,numVar),"\n")
}
```

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