Unified interface for creating machine learning models by wrapping established R packages. This function dispatches to the appropriate underlying package based on the method.
Arguments
- data
A data frame containing the training data
- formula
A formula specifying the model. For unsupervised methods, use
~ varsor NULL.- method
The modeling method. Supervised: "linear" (stats::lm), "logistic" (stats::glm), "tree" (rpart), "forest" (randomForest), "boost" (gbm), "ridge"/"lasso"/"elastic_net" (glmnet), "svm" (e1071), "nn" (nnet), "deep" (keras), "xgboost" (xgboost). Unsupervised: "pca" (stats::prcomp), "mds" (stats/MASS/smacof), "kmeans" (stats::kmeans), "pam"/"clara" (cluster), "hclust" (stats::hclust), "dbscan" (dbscan).
- ...
Additional arguments passed to the underlying model function
- compute
Compute tier for the fit. One of
"cpu"(default, existing behaviour),"gpu"(route to local CUDA when the method has an upstream GPU path – xgboost and deep learning today),"auto"(consulttl_compute_advisorand pick per call), or"cloud"(reserved – not yet wired up). When"gpu"is requested for a method without an upstream GPU path or on a machine without a detected GPU, the call falls back to CPU with a warning.
Value
A tidylearn_model object (S3) containing the fitted model
($fit), model specification ($spec), and training data
($data). The object also inherits from a method-specific class
(e.g., tidylearn_linear) and a paradigm class
(tidylearn_supervised or tidylearn_unsupervised).
Details
The wrapped packages include: stats (lm, glm, prcomp, kmeans, hclust), glmnet, randomForest, xgboost, gbm, e1071, nnet, rpart, cluster, and dbscan. The underlying algorithms are unchanged - this function provides a consistent interface and returns tidy output.
Access the raw model object from the underlying package via model$fit.
Examples
# \donttest{
# Classification -> wraps randomForest::randomForest()
model <- tl_model(iris, Species ~ ., method = "forest")
model$fit # Access the raw randomForest object
#>
#> Call:
#> randomForest(formula = formula, data = data, ntree = ntree, mtry = mtry, importance = importance)
#> Type of random forest: classification
#> Number of trees: 500
#> No. of variables tried at each split: 2
#>
#> OOB estimate of error rate: 4%
#> Confusion matrix:
#> setosa versicolor virginica class.error
#> setosa 50 0 0 0.00
#> versicolor 0 47 3 0.06
#> virginica 0 3 47 0.06
# Regression -> wraps stats::lm()
model <- tl_model(mtcars, mpg ~ wt + hp, method = "linear")
model$fit # Access the raw lm object
#>
#> Call:
#> stats::lm(formula = formula, data = data)
#>
#> Coefficients:
#> (Intercept) wt hp
#> 37.22727 -3.87783 -0.03177
#>
# PCA -> wraps stats::prcomp()
model <- tl_model(iris, ~ ., method = "pca")
model$fit # Access the raw prcomp object
#> $scores
#> # A tibble: 150 × 5
#> .obs_id PC1 PC2 PC3 PC4
#> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 1 -2.26 -0.478 0.127 0.0241
#> 2 2 -2.07 0.672 0.234 0.103
#> 3 3 -2.36 0.341 -0.0441 0.0283
#> 4 4 -2.29 0.595 -0.0910 -0.0657
#> 5 5 -2.38 -0.645 -0.0157 -0.0358
#> 6 6 -2.07 -1.48 -0.0269 0.00659
#> 7 7 -2.44 -0.0475 -0.334 -0.0367
#> 8 8 -2.23 -0.222 0.0884 -0.0245
#> 9 9 -2.33 1.11 -0.145 -0.0268
#> 10 10 -2.18 0.467 0.253 -0.0398
#> # ℹ 140 more rows
#>
#> $loadings
#> # A tibble: 4 × 5
#> variable PC1 PC2 PC3 PC4
#> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 Sepal.Length 0.521 -0.377 0.720 0.261
#> 2 Sepal.Width -0.269 -0.923 -0.244 -0.124
#> 3 Petal.Length 0.580 -0.0245 -0.142 -0.801
#> 4 Petal.Width 0.565 -0.0669 -0.634 0.524
#>
#> $variance_explained
#> # A tibble: 4 × 5
#> component sdev variance prop_variance cum_variance
#> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 PC1 1.71 2.92 0.730 0.730
#> 2 PC2 0.956 0.914 0.229 0.958
#> 3 PC3 0.383 0.147 0.0367 0.995
#> 4 PC4 0.144 0.0207 0.00518 1
#>
#> $model
#> Standard deviations (1, .., p=4):
#> [1] 1.7083611 0.9560494 0.3830886 0.1439265
#>
#> Rotation (n x k) = (4 x 4):
#> PC1 PC2 PC3 PC4
#> Sepal.Length 0.5210659 -0.37741762 0.7195664 0.2612863
#> Sepal.Width -0.2693474 -0.92329566 -0.2443818 -0.1235096
#> Petal.Length 0.5804131 -0.02449161 -0.1421264 -0.8014492
#> Petal.Width 0.5648565 -0.06694199 -0.6342727 0.5235971
#>
#> $settings
#> $settings$scale
#> [1] TRUE
#>
#> $settings$center
#> [1] TRUE
#>
#> $settings$method
#> [1] "prcomp"
#>
#>
# Clustering -> wraps stats::kmeans()
model <- tl_model(iris, method = "kmeans", k = 3)
model$fit # Access the raw kmeans object
#> $clusters
#> # A tibble: 150 × 2
#> .obs_id cluster
#> <chr> <int>
#> 1 1 1
#> 2 2 1
#> 3 3 1
#> 4 4 1
#> 5 5 1
#> 6 6 1
#> 7 7 1
#> 8 8 1
#> 9 9 1
#> 10 10 1
#> # ℹ 140 more rows
#>
#> $centers
#> # A tibble: 3 × 5
#> cluster Sepal.Length Sepal.Width Petal.Length Petal.Width
#> <int> <dbl> <dbl> <dbl> <dbl>
#> 1 1 5.01 3.43 1.46 0.246
#> 2 2 5.90 2.75 4.39 1.43
#> 3 3 6.85 3.07 5.74 2.07
#>
#> $metrics
#> # A tibble: 1 × 6
#> k tot_withinss betweenss tot_ss iter converged
#> <dbl> <dbl> <dbl> <dbl> <int> <lgl>
#> 1 3 78.9 603. 681. 3 TRUE
#>
#> $model
#> K-means clustering with 3 clusters of sizes 50, 62, 38
#>
#> Cluster means:
#> Sepal.Length Sepal.Width Petal.Length Petal.Width
#> 1 5.006000 3.428000 1.462000 0.246000
#> 2 5.901613 2.748387 4.393548 1.433871
#> 3 6.850000 3.073684 5.742105 2.071053
#>
#> Clustering vector:
#> [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#> [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 3 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
#> [75] 2 2 2 3 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 2 3 3 3 3 2 3 3 3 3
#> [112] 3 3 2 2 3 3 3 3 2 3 2 3 2 3 3 2 2 3 3 3 3 3 2 3 3 3 3 2 3 3 3 2 3 3 3 2 3
#> [149] 3 2
#>
#> Within cluster sum of squares by cluster:
#> [1] 15.15100 39.82097 23.87947
#> (between_SS / total_SS = 88.4 %)
#>
#> Available components:
#>
#> [1] "cluster" "centers" "totss" "withinss" "tot.withinss"
#> [6] "betweenss" "size" "iter" "ifault"
#>
# }