PIE (version 1.0.0)

MAE: MAE: Mean Absolute Error

Description

This function calculates the mean absolute error between the predicted values and the true values. The formula for MAE is: $$MAE = \frac{1}{n} \sum_i |y_i - \hat{y}_i|$$

Usage

MAE(pred, true_label)

Value

A numeric value representing the mean absolute error (MAE).

Arguments

pred

The predicted values of the dataset.

true_label

The actual target values of the dataset.

Examples

# \donttest{
# Load the training data
data("winequality")

# Which columns are numerical?
num_col <- 1:11
# Which columns are categorical?
cat_col <- 12
# Which column is the response?
y_col <- ncol(winequality)

# Data Processing (the first 200 rows are sampled for demonstration)
dat <- data_process(X = as.matrix(winequality[1:200, -y_col]), 
  y = winequality[1:200, y_col], 
  num_col = num_col, cat_col = cat_col, y_col = y_col)

# Fit a PIE model
fold <- 1
fit <- PIE_fit(
  X = dat$spl_train_X[[fold]],
  y = dat$train_y[[fold]],
  lasso_group = dat$lasso_group,
  X_orig = dat$orig_train_X[[fold]],
  lambda1 = 0.01, lambda2 = 0.01, iter = 5, eta = 0.05, nrounds = 200
)

# Prediction
pred <- predict(fit, 
  X = dat$spl_validation_X[[fold]],
  X_orig = dat$orig_validation_X[[fold]]
)

# Validation
val_rrmae_test <- MAE(pred$total, dat$validation_y[[fold]])
# }