unifiedml in R: a unified machine learning interface | R bloggers

A unified machine learning interface for R

Overview

unifiedml provides a consistent, sklearn-like interface for virtually any machine learning model in R.

It eliminates the need to remember different function signatures in packages automatically detect the correct interface (formula vs matrix) and type of task (regression versus classification).

Main features

For now:

• Automatic task detection: automatically detects regression versus classification based on the type of response variable (numeric → regression, factor → classification)
• Universal interface: Works seamlessly with glmnet, randomForest, e1071::svmand other popular ML packages with formula or matrix interfaces
• Built-in cross-validation: Consistent cross_val_score() function with automatic metric selection
• Model interpretability: Numerical derivatives and testing of statistical significance via summary()
• Partial dependency plots: Visualize feature effects with plot()
• Method chaining: Clean, pipeable syntax with R6 classes

devtools::install_github("Techtonique/unifiedml")

install.packages(c("glmnet", "randomForest"))

install.packages("e1071")

library(unifiedml) # this package
library(glmnet)
library(randomForest)
library(e1071)

# ------------------------------------------------------------
# REGRESSION EXAMPLES
# ------------------------------------------------------------

cat("\n=== REGRESSION EXAMPLES ===\n\n")

# Example 1: Synthetic data (numeric y → automatic regression)
set.seed(123)
X <- MASS::Boston[, -ncol(MASS::Boston)]
y <- MASS::Boston$medv

# glmnet regression
cat("1. Ridge Regression (glmnet) - Auto-detected: Regression\n")
mod1 <- Model$new(glmnet::glmnet)  # No task parameter needed!
mod1$fit(X, y, alpha = 0, lambda = 0.1)
mod1$print()
cat("\nPredictions:\n")
print(head(mod1$predict(X)))
cat("\n")
mod1$summary()
mod1$plot(feature = 1)
(cv1 <- cross_val_score(mod1, X, y, cv = 5L))  # Auto-uses RMSE
cat("\nMean RMSE:", mean(cv1), "\n\n")

# randomForest regression
cat("\n2. Random Forest Regression - Auto-detected: Regression\n")
mod2 <- Model$new(randomForest::randomForest)  # No task parameter!
mod2$fit(X, y, ntree = 50)
mod2$print()
cat("\n")
mod2$summary(h = 0.01)

# ------------------------------------------------------------
# CLASSIFICATION EXAMPLES
# ------------------------------------------------------------

cat("\n\n=== CLASSIFICATION EXAMPLES ===\n\n")

# Example: Iris dataset (factor y → automatic classification)
data(iris)

# Binary classification with factor
cat("3. Binary Classification with Factor Response\n")
iris_binary <- iris[iris$Species %in% c("setosa", "versicolor"), ]
X_binary <- as.matrix(iris_binary[, 1:4])
y_binary <- iris_binary$Species  # factor → classification

# Multi-class classification
cat("4. Multi-class Classification\n")
X_multi <- as.matrix(iris[, 1:4])
y_multi <- iris$Species  # factor with 3 levels → multi-class classification

mod4 <- Model$new(randomForest::randomForest)  # No task parameter!
mod4$fit(X_multi, y_multi, ntree = 50)
mod4$print()
(cv4 <- cross_val_score(mod4, X_multi, y_multi, cv = 5L))  # Auto-uses accuracy
cat("\nMean Accuracy:", mean(cv4), "\n")


y_multi_numeric <- as.numeric(y_multi)
mod4 <- Model$new(glmnet::glmnet)  # No task parameter!
mod4$fit(X_multi, y_multi_numeric, family="multinomial")
mod4$print()
(cv4 <- cross_val_score(mod4, X_multi, y_multi_numeric, cv = 5L))  # Auto-uses accuracy
cat("\nMean Accuracy:", mean(cv4), "\n")

=== REGRESSION EXAMPLES ===

1. Ridge Regression (glmnet) - Auto-detected: Regression
Model Object
------------
Model function: self$model_fn 
Fitted: TRUE 
Task: regression 
Training samples: 506 
Features: 13 

Predictions:
       1        2        3        4        5        6 
30.12476 25.01360 30.57030 28.68765 28.04710 25.31151 


Model Summary - Numerical Derivatives
======================================
Task: regression 
Samples: 506 | Features: 13 
Step size (h): 0.01 

 Feature Mean_Derivative    Std_Error       t_value p_value Significance
    crim   -1.032452e-01 7.837905e-15 -1.317256e+13       0          ***
      zn    4.322719e-02 5.216968e-15  8.285883e+12       0          ***
   indus    2.743581e-03 1.273890e-14  2.153704e+11       0          ***
    chas    2.753495e+00 6.050646e-15  4.550745e+14       0          ***
     nox   -1.656232e+01 1.293028e-14 -1.280894e+15       0          ***
      rm    3.868607e+00 9.300418e-15  4.159605e+14       0          ***
     age   -4.129908e-04 1.108270e-14 -3.726444e+10       0          ***
     dis   -1.411492e+00 4.776813e-15 -2.954882e+14       0          ***
     rad    2.655385e-01 7.851465e-15  3.382025e+13       0          ***
     tax   -1.038490e-02 8.323530e-15 -1.247656e+12       0          ***
 ptratio   -9.325559e-01 6.254096e-15 -1.491112e+14       0          ***
   black    9.272792e-03 3.489110e-15  2.657638e+12       0          ***
   lstat   -5.149643e-01 4.667193e-15 -1.103371e+14       0          ***

Significance codes: 0 '***' 0.01 '**' 0.05 '*' 0.1 ' ' 1
  |======================================================================| 100%

1. 5.25548773596251
2. 6.05059820073108
3. 6.14742810453446
4. 5.05465660887574
5. 4.9428693648005

Mean RMSE: 5.490208 


2. Random Forest Regression - Auto-detected: Regression
Model Object
------------
Model function: self$model_fn 
Fitted: TRUE 
Task: regression 
Training samples: 506 
Features: 13 


Model Summary - Numerical Derivatives
======================================
Task: regression 
Samples: 506 | Features: 13 
Step size (h): 0.01 

 Feature Mean_Derivative   Std_Error    t_value      p_value Significance
    crim      0.27808959 0.329662424  0.8435587 3.993155e-01             
      zn      0.00000000 0.000000000        NaN          NaN         
   indus      0.03961792 0.030839187  1.2846616 1.994995e-01            *
    chas      0.00000000 0.000000000        NaN          NaN         
     nox     -7.59855104 1.939451552 -3.9178865 1.016265e-04          ***
      rm      4.33226614 0.538906676  8.0389914 6.471192e-15          ***
     age     -0.02569829 0.023089867 -1.1129682 2.662516e-01             
     dis     -0.71310569 0.405677973 -1.7578122 7.938528e-02           **
     rad      0.01739130 0.017453435  0.9964402 3.195135e-01             
     tax     -0.01361660 0.008303022 -1.6399573 1.016368e-01            *
 ptratio     -0.03075099 0.024143264 -1.2736881 2.033599e-01             
   black      0.05649539 0.045334095  1.2462009 2.132684e-01             
   lstat     -0.47499012 0.124407440 -3.8180202 1.512437e-04          ***

Significance codes: 0 '***' 0.01 '**' 0.05 '*' 0.1 ' ' 1


=== CLASSIFICATION EXAMPLES ===

3. Binary Classification with Factor Response
4. Multi-class Classification
Model Object
------------
Model function: self$model_fn 
Fitted: TRUE 
Task: classification 
Training samples: 150 
Features: 4 
Classes: setosa, versicolor, virginica 
Class distribution:

    setosa versicolor  virginica 
        50         50         50 
  |======================================================================| 100%

1. 0.966666666666667
2. 0.933333333333333
3. 1
4. 0.966666666666667
5. 0.933333333333333

Mean Accuracy: 0.96 
Model Object
------------
Model function: self$model_fn 
Fitted: TRUE 
Task: regression 
Training samples: 150 
Features: 4 
  |======================================================================| 100%

1. 0.318625110272249
2. 0.350013640071924
3. 0.319135702141902
4. 0.27769983668844
5. 0.348586362350429

Mean Accuracy: 0.3228121 

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