unifiedml

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 across packages by automatically detecting the appropriate interface (formula vs matrix) and task type (regression vs classification).

Key Features

For now:

• Automatic Task Detection: Automatically detects regression vs classification based on response variable type (numeric → regression, factor → classification)
• Universal Interface: Works seamlessly with glmnet, randomForest, e1071::svm, and other popular ML packages with formula or matrix interface
• Built-in Cross-Validation: Consistent cross_val_score() function with automatic metric selection
• Model Interpretability: Numerical derivatives and statistical significance testing via summary()
• Partial Dependence 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 packagelibrary(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$medv# glmnet regressioncat("1. Ridge Regression (glmnet) - Auto-detected: Regression\n")mod1 <- Model$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$plot(feature = 1)(cv1 <- cross_val_score(mod1, X, y, cv = 5L))  # Auto-uses RMSEcat("\nMean RMSE:", mean(cv1), "\n\n")# randomForest regressioncat("\n2. Random Forest Regression - Auto-detected: Regression\n")mod2 <- Model$new(randomForest::randomForest)  # No task parameter!mod2$fit(X, y, ntree = 50)mod2$print()cat("\n")mod2$summary(h = 0.01)# ------------------------------------------------------------# CLASSIFICATION EXAMPLES# ------------------------------------------------------------cat("\n\n=== CLASSIFICATION EXAMPLES ===\n\n")# Example: Iris dataset (factor y → automatic classification)data(iris)# Binary classification with factorcat("3. Binary Classification with Factor Response\n")iris_binary <- iris[iris$Species %in% c("setosa", "versicolor"), ]X_binary <- as.matrix(iris_binary[, 1:4])y_binary <- iris_binary$Species  # factor → classification# Multi-class classificationcat("4. Multi-class Classification\n")X_multi <- as.matrix(iris[, 1:4])y_multi <- iris$Species  # factor with 3 levels → multi-class classificationmod4 <- Model$new(randomForest::randomForest)  # No task parameter!mod4$fit(X_multi, y_multi, ntree = 50)mod4$print()(cv4 <- cross_val_score(mod4, X_multi, y_multi, cv = 5L))  # Auto-uses accuracycat("\nMean Accuracy:", mean(cv4), "\n")y_multi_numeric <- as.numeric(y_multi)mod4 <- Model$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 accuracycat("\nMean Accuracy:", mean(cv4), "\n")=== REGRESSION EXAMPLES ===1. Ridge Regression (glmnet) - Auto-detected: RegressionModel 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%

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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: RegressionModel Object------------Model function: self$model_fn Fitted: TRUE Task: regression Training samples: 506 Features: 13 Model Summary - Numerical Derivatives======================================Task: regression Samples: 506 | Features: 13 Step size (h): 0.01  Feature Mean_Derivative   Std_Error    t_value      p_value Significance    crim      0.27808959 0.329662424  0.8435587 3.993155e-01                   zn      0.00000000 0.000000000        NaN          NaN         <NA>   indus      0.03961792 0.030839187  1.2846616 1.994995e-01            *    chas      0.00000000 0.000000000        NaN          NaN         <NA>     nox     -7.59855104 1.939451552 -3.9178865 1.016265e-04          ***      rm      4.33226614 0.538906676  8.0389914 6.471192e-15          ***     age     -0.02569829 0.023089867 -1.1129682 2.662516e-01                  dis     -0.71310569 0.405677973 -1.7578122 7.938528e-02           **     rad      0.01739130 0.017453435  0.9964402 3.195135e-01                  tax     -0.01361660 0.008303022 -1.6399573 1.016368e-01            * ptratio     -0.03075099 0.024143264 -1.2736881 2.033599e-01                black      0.05649539 0.045334095  1.2462009 2.132684e-01                lstat     -0.47499012 0.124407440 -3.8180202 1.512437e-04          ***Significance codes: 0 '***' 0.01 '**' 0.05 '*' 0.1 ' ' 1=== CLASSIFICATION EXAMPLES ===3. Binary Classification with Factor Response4. Multi-class ClassificationModel Object------------Model function: self$model_fn Fitted: TRUE Task: classification Training samples: 150 Features: 4 Classes: setosa, versicolor, virginica Class distribution:    setosa versicolor  virginica         50         50         50   |======================================================================| 100%

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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%

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

Mean Accuracy: 0.3228121 

Continue reading: unifiedml in R: A Unified Machine Learning Interface