Reporting with tidylearn

Overview

Two families turn a fitted model into something publishable. plot() and the tl_plot_*() functions return ggplot2 objects; tl_table() and the tl_table_*() functions return gt tables. Both dispatch on model type, so the same call covers a forest and a lasso fit, and both hand back an object you can keep editing rather than printed output you cannot.

library(tidylearn)
library(dplyr)
library(ggplot2)
library(gt)

Plots

plot() picks the visualisation from the model type, and type narrows it further where a model supports more than one view.

Regression

model_reg <- tl_model(mtcars, mpg ~ wt + hp, method = "linear")

# Actual vs predicted — one call
plot(model_reg, type = "actual_predicted")

Classification

split <- tl_split(iris, prop = 0.7, stratify = "Species", seed = 42)
model_clf <- tl_model(split$train, Species ~ ., method = "forest")

plot(model_clf, type = "confusion")

PCA

pca <- tidy_pca(USArrests, scale = TRUE)

tidy_pca_screeplot(pca)

tidy_pca_biplot(pca, label_obs = TRUE)

Regularisation

model_lasso <- tl_model(mtcars, mpg ~ ., method = "lasso")

tl_plot_regularization_path(model_lasso)

tl_plot_regularization_cv(model_lasso)

Tables

tl_table() mirrors the plot interface, dispatching on model type and an optional type:

tl_table(model)                       # auto-selects the best table type
tl_table(model, type = "coefficients") # specific type

Evaluation Metrics

tl_table_metrics(model_reg)
Model Evaluation Metrics
Metric Value
Rmse 2.4689
Mae 1.9015
Rsq 0.8268
tidylearn | linear (regression) | mpg ~ wt + hp | n = 32

Coefficients

For linear and logistic models, the table includes standard errors, test statistics, and p-values, with significant terms highlighted:

tl_table_coefficients(model_reg)
Linear Model Coefficients
Term Estimate Std. Error t value p
(Intercept) 37.2273 1.5988 23.2847 2.57 × 10−20 *
wt −3.8778 0.6327 −6.1287 1.12 × 10−6 *
hp −0.0318 0.0090 −3.5187 1.45 × 10−3 *
tidylearn | linear (regression) | mpg ~ wt + hp | n = 32

For regularised models, coefficients are sorted by magnitude and zero coefficients are greyed out:

tl_table_coefficients(model_lasso)
Lasso Coefficients
lambda = 1.399 (1se)
Term Coefficient |Coefficient|
(Intercept) 33.9411 33.9411
wt −2.3645 2.3645
cyl −0.8434 0.8434
hp −0.0070 0.0070
disp 0.0000 0.0000
drat 0.0000 0.0000
qsec 0.0000 0.0000
vs 0.0000 0.0000
am 0.0000 0.0000
gear 0.0000 0.0000
carb 0.0000 0.0000
tidylearn | lasso (regression) | mpg ~ . | n = 32

Confusion Matrix

A formatted confusion matrix with correct predictions highlighted on the diagonal:

tl_table_confusion(model_clf, new_data = split$test)
Confusion Matrix
Actual
Predicted
setosa versicolor virginica
setosa 15 0 0
versicolor 0 15 0
virginica 0 2 13
tidylearn | forest (classification) | Species ~ . | n = 105

Feature Importance

A ranked importance table with a colour gradient:

tl_table_importance(model_clf)
Feature Importance
Top 4 features
Feature Importance
Petal.Length 100.00
Petal.Width 90.41
Sepal.Length 29.86
Sepal.Width 13.11
tidylearn | forest (classification) | Species ~ . | n = 105

PCA Variance Explained

Cumulative variance is coloured green to highlight how many components are needed:

pca_model <- tl_model(USArrests, method = "pca")
tl_table_variance(pca_model)
PCA Variance Explained
Component Std. Dev. Variance Proportion Cumulative
PC1 1.5749 2.4802 62.0% 62.0%
PC2 0.9949 0.9898 24.7% 86.8%
PC3 0.5971 0.3566 8.9% 95.7%
PC4 0.4164 0.1734 4.3% 100.0%
tidylearn | pca | n = 50

PCA Loadings

A diverging red–blue colour scale highlights strong positive and negative loadings:

tl_table_loadings(pca_model)
PCA Loadings
Variable PC1 PC2 PC3 PC4
Murder −0.536 −0.418 0.341 0.649
Assault −0.583 −0.188 0.268 −0.743
UrbanPop −0.278 0.873 0.378 0.134
Rape −0.543 0.167 −0.818 0.089
tidylearn | pca | n = 50

Cluster Summary

Cluster sizes and mean feature values:

km <- tl_model(iris[, 1:4], method = "kmeans", k = 3)
tl_table_clusters(km)
Cluster Summary
kmeans | 3 clusters
Cluster Size Sepal.Length Sepal.Width Petal.Length Petal.Width
1 50 5.01 3.43 1.46 0.25
2 38 6.85 3.07 5.74 2.07
3 62 5.90 2.75 4.39 1.43
tidylearn | kmeans | n = 150

Model Comparison

Compare multiple models side-by-side:

m1 <- tl_model(split$train, Species ~ ., method = "svm")
m2 <- tl_model(split$train, Species ~ ., method = "forest")
m3 <- tl_model(split$train, Species ~ ., method = "tree")

tl_table_comparison(
  m1, m2, m3,
  new_data = split$test,
  names = c("SVM", "Random Forest", "Decision Tree")
)
Model Comparison
3 models compared
Metric SVM Random Forest Decision Tree
Accuracy 0.9111 0.9333 0.8889
tidylearn | n = 45

Interactive Reporting with plotly

Every plot function returns a ggplot2 object, so ggplotly() takes any of them without special handling:

library(plotly)

ggplotly(plot(model_reg, type = "actual_predicted"))
ggplotly(tidy_pca_biplot(pca, label_obs = TRUE))
ggplotly(tl_plot_regularization_path(model_lasso))

Putting It Together

Fit, score, look, drill in — the four calls that make up most reporting sections:

# Fit
model <- tl_model(split$train, Species ~ ., method = "forest")

# Evaluate
tl_table_metrics(model, new_data = split$test)
Model Evaluation Metrics
Metric Value
Accuracy 0.9333
tidylearn | forest (classification) | Species ~ . | n = 105 

# Visualise
plot(model, type = "confusion")


# Drill into feature importance
tl_table_importance(model, top_n = 4)
Feature Importance
Top 4 features
Feature Importance
Petal.Length 100.00
Petal.Width 91.03
Sepal.Length 28.44
Sepal.Width 12.53
tidylearn | forest (classification) | Species ~ . | n = 105

Swap method = "forest" for method = "tree" or method = "svm" and the reporting code above works without modification.