The basics | Engines | Local models and hosted services | What a recipe stores | Speed, caching, and tuning | Limitations

The basics | Engines and tokens | The tokenizers engine | Words | Penn Treebank | Characters and character shingles | N-grams and skip n-grams | Sentences, lines, and paragraphs | Regular expressions | Word stems | Training a tokenizer | Other engines | Custom tokenizers | Choosing a tokenizer | Next steps

Creating a sparse matrix | Steps that produce sparse data | Controlling the number of columns | Using workflows instead

Supported models | Why some models support one classification type but not the other | Ensembles | Recipes steps | tailor adjustments

Supported layers | Input names | Mode, type, and lvl | Extracting an intermediate layer | Multi-output networks | torch | keras3

Group-awareness | Groupwise metrics

Example | Metrics

Introduction | Macro averaging | Micro averaging | Specialized multiclass implementations

The problem | The solution | Batched summation | Example | Supported models | Models where the argument has no effect | Output behavior | When to use | Good candidates for separate_trees = TRUE | When to stick with the default | Tradeoffs | Benchmarking recommendation

Introduction | The tidyclust workflow | K-means example | 1. Create a specification | 2. Fit to data | 3. Extract results | 4. Evaluate | Hierarchical clustering example | Tidymodels integration | Next steps

About this case study | Overview | Problem statement | User prompt | Iteration 1: Initial profiling | Running debrief | Debrief output (key sections) | How I interpreted this output | Optimization applied | Result | Iteration 2: Finding the next bottleneck | Debrief output after first optimization | Iteration 3: Diminishing returns | Debrief output (using 10 trees for better sampling) | Why I only used pv_print_debrief() | Functions I didn't use | When pv_print_debrief() alone is sufficient | When to use the other functions | Final results

How lime explains stuff | How to permute an observation | Calculating similarities with permutations | Selecting the features to use | Fitting a model to the permuted and feature-reduced data | An example - Tabular Data | An example - text data | Interactive text model explanations | Session Info

Altrep Functions | Converting Functions | Helper Functions | FAQ

broom: let's tidy up a bit | Tidying functions | Other Examples | Generalized linear and non-linear models | Hypothesis testing | Conventions | All functions | tidy functions | augment functions | glance functions

Counting select words | Removing words in addition to the stop words list | Letter distributions | TF-IDF of ngrams of stemmed tokens

tokens attribute | lemma and pos attributes

Only using step_tokenize() | Using step_tokenize() and step_ngram()

Load pretrained model | Printing word vectors | Printing sentence vectors | Nearest neighbor queries | Word analogies | References

Download Data | Normalize Data | Train Model | Read Model | Predict / Test Model | References

Guidelines for textdata datasets | Classification datasets

Tidying k-means clustering

Variable paragraph length | Show paper | Show page number | Variable page length | Centering | Paragraph to line