Unified Machine Learning Interface using R6

Provides a consistent interface for various machine learning models in R, with automatic detection of formula vs matrix interfaces, built-in cross-validation, model interpretability, and visualization.

An R6 class that provides a unified interface for regression and classification models with automatic interface detection, cross-validation, and interpretability features. The task type (regression vs classification) is automatically detected from the response variable type.

Author

Your Name

Public fields

model_fn

The modeling function (e.g., glmnet::glmnet, randomForest::randomForest)

fitted

The fitted model object

task

Type of task: "regression" or "classification" (automatically detected)

X_train

Training features matrix

y_train

Training target vector

Methods

Public methods

• Model$new()

• Model$fit()

• Model$predict()

• Model$print()

• Model$summary()

• Model$plot()

• Model$clone_model()

• Model$clone()

---

Method new()

Initialize a new Model

Usage

Model$new(model_fn)

Arguments

model_fn

A modeling function (e.g., glmnet, randomForest, svm)

Returns

A new Model object

---

Method fit()

Fit the model to training data

Automatically detects task type (regression vs classification) based on the type of the response variable y. Numeric y -> regression, factor y -> classification.

Usage

Model$fit(X, y, ...)

Arguments

X

Feature matrix or data.frame

y

Target vector (numeric for regression, factor for classification)

...

Additional arguments passed to the model function

Returns

self (invisible) for method chaining

---

Method predict()

Generate predictions from fitted model

Usage

Model$predict(X, type = NULL, ...)

Arguments

X

Feature matrix for prediction

type

Type of prediction ("response", "class", "probabilities")

...

Additional arguments passed to predict function

Returns

Vector of predictions

---

Method print()

Print model information

Usage

Model$print()

Returns

self (invisible) for method chaining

---

Method summary()

Compute numerical derivatives and statistical significance

Uses finite differences to compute approximate partial derivatives for each feature, providing model-agnostic interpretability.

Usage

Model$summary(h = 0.01, alpha = 0.05)

Arguments

h

Step size for finite differences (default: 0.01)

alpha

Significance level for p-values (default: 0.05)

Details

The method computes numerical derivatives using central differences.

Statistical significance is assessed using t-tests on the derivative estimates across samples.

Returns

A data.frame with derivative statistics (invisible)

---

Method plot()

Create partial dependence plot for a feature

Visualizes the relationship between a feature and the predicted outcome while holding other features at their mean values.

Usage

Model$plot(feature = 1, n_points = 100)

Arguments

feature

Index or name of feature to plot

n_points

Number of points for the grid (default: 100)

Returns

self (invisible) for method chaining

---

Method clone_model()

Create a deep copy of the model

Useful for cross-validation and parallel processing where multiple independent model instances are needed.

Usage

Model$clone_model()

Returns

A new Model object with same configuration

---

Method clone()

The objects of this class are cloneable with this method.

Usage

Model$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

if (FALSE) { # \dontrun{
# Regression example with glmnet
library(glmnet)
X <- matrix(rnorm(100), ncol = 4)
y <- 2*X[,1] - 1.5*X[,2] + rnorm(25)  # numeric → regression

mod <- Model$new(glmnet::glmnet)
mod$fit(X, y, alpha = 0, lambda = 0.1)
mod$summary()
predictions <- mod$predict(X)

# Classification example  
data(iris)
iris_binary <- iris[iris$Species %in% c("setosa", "versicolor"), ]
X_class <- as.matrix(iris_binary[, 1:4])
y_class <- iris_binary$Species  # factor → classification

mod2 <- Model$new(e1071::svm)
mod2$fit(X_class, y_class, kernel = "radial")
mod2$summary()

# Cross-validation
cv_scores <- cross_val_score(mod, X, y, cv = 5)
} # }