How it works | Under the hood | Categorical predictors | parsnip | Boosted models | Rule-based models | Limitations

How it works | Under the hood | Categorical predictors | Classification | Limitations

How it works | Under the hood | A note on split boundaries | parsnip

How it works | Under the hood | parsnip

Highlights & Limitations | How it works | Under the hood | How it performs | parsnip | LiblineaR

Highlights & Limitations | How it works | Prediction intervals | Under the hood | How it performs | parsnip | Quantile regression

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

The problem | The solution | Batched summation | Example | Supported models | Output behavior | When to use | Good candidates for separate_trees = TRUE | When to stick with the default | Tradeoffs | Benchmarking recommendation

Supported models | Recipes steps | tailor adjustments

tidypredict_ functions | Supported objectives | Regression objectives (identity transform) | Binary classification (sigmoid transform) | Multiclass classification | Binary classification example | Multiclass classification example | Categorical features | With parsnip/bonsai (recommended) | With raw CatBoost | Parse model spec | Limitations

tidypredict_ functions | Parse model spec | Limitations

The issue | Which models are affected? | Example | What tidypredict does | Pros and cons | Recommendations

tidypredict_ functions | Supported objectives | Regression objectives (identity transform) | Regression objectives (exp transform) | Binary classification (sigmoid transform) | Multiclass classification | Binary classification example | Categorical features | parsnip | Parse model spec | Limitations

tidypredict_ functions | parsnip | Parse model spec

Nested vs flat case_when | Flat case_when (old approach) | Nested case_when (current approach) | Why nested is better | Parsed model versions

How it works | Under the hood | Classification | parsnip | Categorical predictors | Surrogate splits

Example | Metrics

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

How it works | Under the hood | parsnip

How it works | Under the hood | parsnip

tidypredict_ functions | parsnip | Parse model spec

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

Group-awareness | Groupwise metrics

Introduction | Macro averaging | Micro averaging | Specialized multiclass implementations

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

Example setup | Model preparation | Scenario 1 - Update scores | Scenario 2- Append new scores

tidypredict_ functions | GLM models | parsnip | Parse model spec

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

python example | Read in R | tidypredict | broom

Parse model | Saving the model | Re-load the model | broom

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