Create a regression model using tisthemachinelearner

regressor(x, y, model_name, calibration = FALSE, seed = 42, ...)

Arguments

x: Feature matrix
y: Target vector
model_name: Name of the sklearn model to use
calibration: Logical flag to indicate if calibration of residuals should be used
seed: Seed for the random number generator
...: Additional parameters passed to the sklearn model

Value

A regressor object

Examples

# Split features and target
X <- as.matrix(mtcars[, -1])  # all columns except mpg
y <- mtcars[, 1]              # mpg column

# Create train/test split
set.seed(42)
train_idx <- sample(nrow(mtcars), size = floor(0.8 * nrow(mtcars)))
X_train <- X[train_idx, ]
X_test <- X[-train_idx, ]
y_train <- y[train_idx]
y_test <- y[-train_idx]

# Fit linear regression model
reg_linear <- regressor(X_train, y_train, "LinearRegression")

# Make predictions
predictions <- predict(reg_linear, X_test)

# Calculate RMSE
(rmse <- sqrt(mean((predictions - y_test)^2)))
#> [1] 4.876167