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). The method and the response have to agree, and a mismatch is an error rather than a meaningless fit:
"linear"and"polynomial"need a numeric response,"logistic"needs exactly two classes, and every other supervised method takes either. 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, or $fit$model for an unsupervised method),
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.
For a supervised method, model$fit is the object the wrapped
function returned. An unsupervised method returns tidied components as
well, so the wrapped object sits at model$fit$model and
model$fit is the list holding both.
For classification, the response is reduced to the classes it actually contains: subsetting a data frame keeps every factor level, and a level no row uses would otherwise be reported as a class, given its own (zero) probability column, and counted when deciding whether the problem is binary. The fit is unaffected.
Examples
# \donttest{
# Classification -> wraps randomForest::randomForest()
model <- tl_model(iris, Species ~ ., method = "forest")
model$fit # Access the raw randomForest object
#>
#> Call:
#> randomForest(formula = Species ~ ., data = data, ntree = 500, importance = TRUE)
#> Type of random forest: classification
#> Number of trees: 500
#> No. of variables tried at each split: 2
#>
#> OOB estimate of error rate: 4.67%
#> Confusion matrix:
#> setosa versicolor virginica class.error
#> setosa 50 0 0 0.00
#> versicolor 0 47 3 0.06
#> virginica 0 4 46 0.08
# Regression -> wraps stats::lm()
model <- tl_model(mtcars, mpg ~ wt + hp, method = "linear")
model$fit # Access the raw lm object
#>
#> Call:
#> lm(formula = mpg ~ wt + hp, data = data)
#>
#> Coefficients:
#> (Intercept) wt hp
#> 37.22727 -3.87783 -0.03177
#>
# PCA -> wraps stats::prcomp()
model <- tl_model(iris, ~ ., method = "pca")
model$fit$model # The raw prcomp object, alongside tidied components
#> 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
# Clustering -> wraps stats::kmeans()
model <- tl_model(iris, method = "kmeans", k = 3)
model$fit$model # The raw kmeans object
#> 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"
# }