MAE {PIE} | R Documentation |
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)
Arguments
pred |
The predicted values of the dataset. |
true_label |
The actual target values of the dataset. |
Value
A numeric value representing the mean absolute error (MAE).
Examples
# 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]])
[Package PIE version 1.0.0 Index]