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Type: Package
Title: Unified S3 Interface to Machine Learning Models
Version: 0.1.0
Maintainer: T. Moudiki <thierry.moudiki@gmail.com>
Description: Provides a unified and consistent S3 interface for training and predicting with a variety of machine learning models in R. The package wraps popular algorithms (e.g., from 'glmnet', 'lightgbm', 'ranger', 'e1071', and 'caret') under a common workflow based on simple wrap_*() and predict() functions, allowing users to switch between models without changing their code structure. It supports both classification and regression tasks and facilitates rapid experimentation, benchmarking, and comparison of models. By abstracting away package-specific APIs while preserving flexibility in parameter specification, the package streamlines machine learning workflows and promotes reproducibility.
License: GPL-3
Encoding: UTF-8
Imports: e1071, glmnet, lightgbm, ranger
Suggests: caret, knitr, randomForest, kernlab
RoxygenNote: 7.3.2
VignetteBuilder: knitr
Language: en-US
NeedsCompilation: no
Packaged: 2026-04-24 10:15:03 UTC; t
Author: T. Moudiki [aut, cre]
Repository: CRAN
Date/Publication: 2026-04-28 19:00:20 UTC

Predict method for mlS3 caret wrapper

Description

Predict method for mlS3 caret wrapper

Usage

## S3 method for class 'wrap_caret'
predict(object, newx, type = NULL, ...)

Arguments

object

Object from wrap_caret

newx

New features (matrix or data frame)

type

Prediction type: "raw" (default), "class", "prob", or NULL

...

Additional arguments to caret::predict.train

Value

Vector or matrix of predictions

Examples


# Only runs if caret is installed

data(mtcars)

# Prepare data
X_reg <- mtcars[, -1]  # All except mpg
y_reg <- mtcars$mpg     # Target variable

# Split into train/test
set.seed(123)
idx_reg <- sample(nrow(X_reg), 0.7 * nrow(X_reg))
X_train <- X_reg[idx_reg, ]
y_train <- y_reg[idx_reg]
X_test <- X_reg[-idx_reg, ]
y_test <- y_reg[-idx_reg]

mod <- wrap_caret(X_train, y_train, method = "rf", mtry = 3)
(pred <- predict(mod, X_test))

Print method for wrap_caret objects

Description

Print method for wrap_caret objects

Usage

## S3 method for class 'wrap_caret'
print(x, ...)

Arguments

x

Object from wrap_caret

...

Additional arguments

Wrap caret models for mlS3

Description

Minimal wrapper around caret::train with no tuning. Hyperparameters can be passed via ... as named arguments.

Usage

wrap_caret(X, y, method = "rf", ...)

Arguments

X

Feature matrix or data frame

y

Response vector

method

caret model method (default "rf")

...

Named hyperparameters (e.g., mtry = 3, ntree = 500)

Value

Object with class "mlS3_caret"

Examples


# Only runs if caret is installed

data(mtcars)

# Prepare data
X_reg <- mtcars[, -1]  # All except mpg
y_reg <- mtcars$mpg     # Target variable

# Split into train/test
set.seed(123)
idx_reg <- sample(nrow(X_reg), 0.7 * nrow(X_reg))
X_train <- X_reg[idx_reg, ]
y_train <- y_reg[idx_reg]
X_test <- X_reg[-idx_reg, ]
y_test <- y_reg[-idx_reg]

mod <- wrap_caret(X_train, y_train, method = "rf", mtry = 3)
(pred <- predict(mod, X_test))

S3 wrapper for glmnet

Description

Fits a 'glmnet' penalized regression model with a consistent interface. Supports regression and binary classification.

Usage

wrap_glmnet(x, y, ...)

## S3 method for class 'wrap_glmnet'
predict(object, newx, type = c("class", "prob"), s = NULL, ...)

## S3 method for class 'wrap_glmnet'
print(x, ...)

Arguments

x

A matrix or data.frame of features.

y

A factor or character vector for classification, numeric for regression.

...

Additional arguments passed to [glmnet::glmnet()]. Pass 'family = "binomial"' for binary classification.

object

A fitted 'wrap_glmnet' object.

newx

A matrix or data.frame of new observations.

type

'"class"' (default) for class labels, '"prob"' for a probability matrix. Ignored for regression.

s

Lambda value for prediction. Defaults to the midpoint of the lambda path. Pass 's = cv_fit$lambda.min' if using [glmnet::cv.glmnet()].

Value

An object of class 'wrap_glmnet' with fields:

fit

The fitted glmnet model.

levels

Class levels (NULL for regression).

task

"classification" or "regression".

Note

Multiclass ('family = "multinomial"') is not yet supported. For lambda selection, a specific 's' value can be passed to 'predict()'. By default the midpoint of the lambda path is used. For optimal lambda, use [glmnet::cv.glmnet()] externally and pass 's = fit$lambda.min'.

Examples


X <- iris[iris$Species != "virginica", 1:4]
y <- droplevels(iris[iris$Species != "virginica", "Species"])
mod <- wrap_glmnet(X, y, family = "binomial")
predict(mod, newx = X, type = "class")
predict(mod, newx = X, type = "prob")


X <- iris[iris$Species != "virginica", 1:4]
y <- droplevels(iris[iris$Species != "virginica", "Species"])
mod <- wrap_glmnet(X, y, family = "binomial")
predict(mod, newx = X, type = "class")
predict(mod, newx = X, type = "prob")

S3 wrapper for lightgbm

Description

Fits a 'lightgbm' model with a consistent interface. Supports binary classification, multiclass classification, and regression.

Usage

wrap_lightgbm(x, y, ...)

## S3 method for class 'wrap_lightgbm'
predict(object, newx, type = c("class", "prob"), ...)

## S3 method for class 'wrap_lightgbm'
print(x, ...)

Arguments

x

A matrix or data.frame of features.

y

A factor or character vector for classification, numeric for regression.

...

Additional arguments passed to [lightgbm::lgb.train()]. Pass 'params = list(objective = "binary")' for binary classification, 'params = list(objective = "multiclass", num_class = k)' for multiclass, or 'params = list(objective = "regression")' for regression.

object

A fitted 'wrap_lightgbm' object.

newx

A matrix or data.frame of new observations.

type

'"class"' (default) for class labels, '"prob"' for a probability matrix. Ignored for regression.

Value

An object of class 'wrap_lightgbm' with fields:

fit

The fitted lgb.Booster model.

levels

Class levels (NULL for regression).

task

"classification" or "regression".

objective

The lightgbm objective string, stored at fit time.

Examples


library(mlS3)

X <- iris[, 1:4]
y <- iris$Species
mod <- wrap_lightgbm(X, y,
  params = list(objective = "multiclass", num_class = 3, verbose = -1),
  nrounds = 50)
predict(mod, newx = X, type = "class")
predict(mod, newx = X, type = "prob")

S3 wrapper for ranger

Description

Fits a 'ranger' random forest with a consistent interface. Supports both classification (factor 'y') and regression (numeric 'y').

Usage

wrap_ranger(x, y, ...)

## S3 method for class 'wrap_ranger'
predict(object, newx, type = c("class", "prob"), ...)

## S3 method for class 'wrap_ranger'
print(x, ...)

Arguments

x

A matrix or data.frame of features.

y

A factor or character vector for classification, numeric for regression.

...

Additional arguments passed to [ranger::ranger()].

object

A fitted 'wrap_ranger' object.

newx

A matrix or data.frame of new observations.

type

'"class"' (default) for class labels, '"prob"' for a probability matrix. Ignored for regression.

Value

An object of class 'wrap_ranger' with fields:

fit

The fitted ranger model.

levels

Class levels (NULL for regression).

task

"classification" or "regression".

Examples


X <- as.matrix(iris[, 1:4])
y <- iris$Species
mod <- wrap_ranger(X, y, num.trees = 100L)
predict(mod, newx = X, type = "class")
predict(mod, newx = X, type = "prob")


X <- as.matrix(iris[, 1:4])
y <- iris$Species
mod <- wrap_ranger(X, y, num.trees = 100L)
predict(mod, newx = X, type = "class")
predict(mod, newx = X, type = "prob")

S3 wrapper for e1071 SVM

Description

Fits an 'e1071' support vector machine with a consistent interface. Supports classification and regression.

Usage

wrap_svm(x, y, ...)

## S3 method for class 'wrap_svm'
predict(object, newx, type = c("class", "prob"), ...)

## S3 method for class 'wrap_svm'
print(x, ...)

Arguments

x

A matrix or data.frame of features.

y

A factor or character vector for classification, numeric for regression.

...

Additional arguments passed to [e1071::svm()]. 'probability = TRUE' is set automatically for classification; do not override this if you need 'type = "prob"' predictions.

object

A fitted 'wrap_svm' object.

newx

A matrix or data.frame of new observations.

type

'"class"' (default) for class labels, '"prob"' for a probability matrix. Ignored for regression.

Value

An object of class 'wrap_svm' with fields:

fit

The fitted svm model.

levels

Class levels (NULL for regression).

task

"classification" or "regression".

Examples


X <- as.matrix(iris[, 1:4])
y <- iris$Species
mod <- wrap_svm(X, y, kernel = "radial")
predict(mod, newx = X, type = "class")
predict(mod, newx = X, type = "prob")


X <- as.matrix(iris[, 1:4])
y <- iris$Species
mod <- wrap_svm(X, y, kernel = "radial")
predict(mod, newx = X, type = "class")
predict(mod, newx = X, type = "prob")

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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