# latu_ml v0.4.2 - Table of Contents

> Spark MLlib from Elixir, over Spark Connect. A companion package to Latu.

## Pages

- [Latu ML](readme.md)
- [Changelog](changelog.md)
- [Contributing](contributing.md)

- Guides
  - [Quick start](quick-start.md)
  - [Cookbook](cookbook.md)
  - [Coming from PySpark ML](from-pyspark-ml.md)
  - [With Nx and Scholar](with-scholar-and-nx.md)

- Reference
  - [Cheatsheet](cheatsheet.md)
  - [Using Latu ML](usage-rules.md)
  - [Deviations from PySpark](deviations.md)

## Modules

- Verbs
  - [Latu.ML](Latu.ML.md): Spark MLlib from Elixir, over Spark Connect.
  - [Latu.ML.Functions](Latu.ML.Functions.md): Converting between a `Vector` column and an ordinary array of numbers.
  - [Latu.ML.Stat](Latu.ML.Stat.md): `pyspark.ml.stat`: three tests that run where the data is, and `Summarizer`.

- Operators
  - [Latu.ML.Classification](Latu.ML.Classification.md): Classifiers, and the models they fit.
  - [Latu.ML.Clustering](Latu.ML.Clustering.md): Clustering estimators, and the models they fit.
  - [Latu.ML.Evaluation](Latu.ML.Evaluation.md): Evaluators: one metric over a scored frame.
  - [Latu.ML.FPM](Latu.ML.FPM.md): Frequent pattern mining.
  - [Latu.ML.Feature](Latu.ML.Feature.md): Feature transformers, and the models the fitted ones produce.
  - [Latu.ML.Recommendation](Latu.ML.Recommendation.md): Collaborative filtering.
  - [Latu.ML.Regression](Latu.ML.Regression.md): Regressors, and the models they fit.

- Model and summary attributes
  - [Latu.ML.Classification.BinaryLogisticRegressionSummary](Latu.ML.Classification.BinaryLogisticRegressionSummary.md): Attributes of a `BinaryLogisticRegressionSummary` — what an estimator recorded while it fitted. `Latu.ML.summary/1` is what hands you one.
  - [Latu.ML.Classification.BinaryLogisticRegressionTrainingSummary](Latu.ML.Classification.BinaryLogisticRegressionTrainingSummary.md): Attributes of a `BinaryLogisticRegressionTrainingSummary` — what an estimator recorded while it fitted. `Latu.ML.summary/1` is what hands you one.
  - [Latu.ML.Classification.BinaryRandomForestClassificationSummary](Latu.ML.Classification.BinaryRandomForestClassificationSummary.md): Attributes of a `BinaryRandomForestClassificationSummary` — what an estimator recorded while it fitted. `Latu.ML.summary/1` is what hands you one.
  - [Latu.ML.Classification.BinaryRandomForestClassificationTrainingSummary](Latu.ML.Classification.BinaryRandomForestClassificationTrainingSummary.md): Attributes of a `BinaryRandomForestClassificationTrainingSummary` — what an estimator recorded while it fitted. `Latu.ML.summary/1` is what hands you one.
  - [Latu.ML.Classification.DecisionTreeClassificationModel](Latu.ML.Classification.DecisionTreeClassificationModel.md): Attributes of a fitted `DecisionTreeClassificationModel`.
  - [Latu.ML.Classification.FMClassificationModel](Latu.ML.Classification.FMClassificationModel.md): Attributes of a fitted `FMClassificationModel`.
  - [Latu.ML.Classification.FMClassificationSummary](Latu.ML.Classification.FMClassificationSummary.md): Attributes of a `FMClassificationSummary` — what an estimator recorded while it fitted. `Latu.ML.summary/1` is what hands you one.
  - [Latu.ML.Classification.FMClassificationTrainingSummary](Latu.ML.Classification.FMClassificationTrainingSummary.md): Attributes of a `FMClassificationTrainingSummary` — what an estimator recorded while it fitted. `Latu.ML.summary/1` is what hands you one.
  - [Latu.ML.Classification.GBTClassificationModel](Latu.ML.Classification.GBTClassificationModel.md): Attributes of a fitted `GBTClassificationModel`.
  - [Latu.ML.Classification.LinearSVCModel](Latu.ML.Classification.LinearSVCModel.md): Attributes of a fitted `LinearSVCModel`.
  - [Latu.ML.Classification.LinearSVCSummary](Latu.ML.Classification.LinearSVCSummary.md): Attributes of a `LinearSVCSummary` — what an estimator recorded while it fitted. `Latu.ML.summary/1` is what hands you one.
  - [Latu.ML.Classification.LinearSVCTrainingSummary](Latu.ML.Classification.LinearSVCTrainingSummary.md): Attributes of a `LinearSVCTrainingSummary` — what an estimator recorded while it fitted. `Latu.ML.summary/1` is what hands you one.
  - [Latu.ML.Classification.LogisticRegressionModel](Latu.ML.Classification.LogisticRegressionModel.md): Attributes of a fitted `LogisticRegressionModel`.
  - [Latu.ML.Classification.LogisticRegressionSummary](Latu.ML.Classification.LogisticRegressionSummary.md): Attributes of a `LogisticRegressionSummary` — what an estimator recorded while it fitted. `Latu.ML.summary/1` is what hands you one.
  - [Latu.ML.Classification.LogisticRegressionTrainingSummary](Latu.ML.Classification.LogisticRegressionTrainingSummary.md): Attributes of a `LogisticRegressionTrainingSummary` — what an estimator recorded while it fitted. `Latu.ML.summary/1` is what hands you one.
  - [Latu.ML.Classification.MultilayerPerceptronClassificationModel](Latu.ML.Classification.MultilayerPerceptronClassificationModel.md): Attributes of a fitted `MultilayerPerceptronClassificationModel`.
  - [Latu.ML.Classification.MultilayerPerceptronClassificationSummary](Latu.ML.Classification.MultilayerPerceptronClassificationSummary.md): Attributes of a `MultilayerPerceptronClassificationSummary` — what an estimator recorded while it fitted. `Latu.ML.summary/1` is what hands you one.
  - [Latu.ML.Classification.MultilayerPerceptronClassificationTrainingSummary](Latu.ML.Classification.MultilayerPerceptronClassificationTrainingSummary.md): Attributes of a `MultilayerPerceptronClassificationTrainingSummary` — what an estimator recorded while it fitted. `Latu.ML.summary/1` is what hands you one.
  - [Latu.ML.Classification.NaiveBayesModel](Latu.ML.Classification.NaiveBayesModel.md): Attributes of a fitted `NaiveBayesModel`.
  - [Latu.ML.Classification.RandomForestClassificationModel](Latu.ML.Classification.RandomForestClassificationModel.md): Attributes of a fitted `RandomForestClassificationModel`.
  - [Latu.ML.Classification.RandomForestClassificationSummary](Latu.ML.Classification.RandomForestClassificationSummary.md): Attributes of a `RandomForestClassificationSummary` — what an estimator recorded while it fitted. `Latu.ML.summary/1` is what hands you one.
  - [Latu.ML.Classification.RandomForestClassificationTrainingSummary](Latu.ML.Classification.RandomForestClassificationTrainingSummary.md): Attributes of a `RandomForestClassificationTrainingSummary` — what an estimator recorded while it fitted. `Latu.ML.summary/1` is what hands you one.
  - [Latu.ML.Clustering.BisectingKMeansModel](Latu.ML.Clustering.BisectingKMeansModel.md): Attributes of a fitted `BisectingKMeansModel`.
  - [Latu.ML.Clustering.BisectingKMeansSummary](Latu.ML.Clustering.BisectingKMeansSummary.md): Attributes of a `BisectingKMeansSummary` — what an estimator recorded while it fitted. `Latu.ML.summary/1` is what hands you one.
  - [Latu.ML.Clustering.ClusteringSummary](Latu.ML.Clustering.ClusteringSummary.md): Attributes of a `ClusteringSummary` — what an estimator recorded while it fitted. `Latu.ML.summary/1` is what hands you one.
  - [Latu.ML.Clustering.DistributedLDAModel](Latu.ML.Clustering.DistributedLDAModel.md): Attributes of a fitted `DistributedLDAModel`.
  - [Latu.ML.Clustering.GaussianMixtureModel](Latu.ML.Clustering.GaussianMixtureModel.md): Attributes of a fitted `GaussianMixtureModel`.
  - [Latu.ML.Clustering.GaussianMixtureSummary](Latu.ML.Clustering.GaussianMixtureSummary.md): Attributes of a `GaussianMixtureSummary` — what an estimator recorded while it fitted. `Latu.ML.summary/1` is what hands you one.
  - [Latu.ML.Clustering.KMeansModel](Latu.ML.Clustering.KMeansModel.md): Attributes of a fitted `KMeansModel`.
  - [Latu.ML.Clustering.KMeansSummary](Latu.ML.Clustering.KMeansSummary.md): Attributes of a `KMeansSummary` — what an estimator recorded while it fitted. `Latu.ML.summary/1` is what hands you one.
  - [Latu.ML.Clustering.LDAModel](Latu.ML.Clustering.LDAModel.md): Attributes of a fitted `LDAModel`.
  - [Latu.ML.Clustering.LocalLDAModel](Latu.ML.Clustering.LocalLDAModel.md): Attributes of a fitted `LocalLDAModel`.
  - [Latu.ML.FPM.FPGrowthModel](Latu.ML.FPM.FPGrowthModel.md): Attributes of a fitted `FPGrowthModel`.
  - [Latu.ML.Feature.BucketedRandomProjectionLSHModel](Latu.ML.Feature.BucketedRandomProjectionLSHModel.md): Attributes of a fitted `BucketedRandomProjectionLSHModel`.
  - [Latu.ML.Feature.ChiSqSelectorModel](Latu.ML.Feature.ChiSqSelectorModel.md): Attributes of a fitted `ChiSqSelectorModel`.
  - [Latu.ML.Feature.CountVectorizerModel](Latu.ML.Feature.CountVectorizerModel.md): Attributes of a fitted `CountVectorizerModel`.
  - [Latu.ML.Feature.IDFModel](Latu.ML.Feature.IDFModel.md): Attributes of a fitted `IDFModel`.
  - [Latu.ML.Feature.ImputerModel](Latu.ML.Feature.ImputerModel.md): Attributes of a fitted `ImputerModel`.
  - [Latu.ML.Feature.MaxAbsScalerModel](Latu.ML.Feature.MaxAbsScalerModel.md): Attributes of a fitted `MaxAbsScalerModel`.
  - [Latu.ML.Feature.MinHashLSHModel](Latu.ML.Feature.MinHashLSHModel.md): Attributes of a fitted `MinHashLSHModel`.
  - [Latu.ML.Feature.MinMaxScalerModel](Latu.ML.Feature.MinMaxScalerModel.md): Attributes of a fitted `MinMaxScalerModel`.
  - [Latu.ML.Feature.OneHotEncoderModel](Latu.ML.Feature.OneHotEncoderModel.md): Attributes of a fitted `OneHotEncoderModel`.
  - [Latu.ML.Feature.PCAModel](Latu.ML.Feature.PCAModel.md): Attributes of a fitted `PCAModel`.
  - [Latu.ML.Feature.RFormulaModel](Latu.ML.Feature.RFormulaModel.md): Attributes of a fitted `RFormulaModel`.
  - [Latu.ML.Feature.RobustScalerModel](Latu.ML.Feature.RobustScalerModel.md): Attributes of a fitted `RobustScalerModel`.
  - [Latu.ML.Feature.StandardScalerModel](Latu.ML.Feature.StandardScalerModel.md): Attributes of a fitted `StandardScalerModel`.
  - [Latu.ML.Feature.StringIndexerModel](Latu.ML.Feature.StringIndexerModel.md): Attributes of a fitted `StringIndexerModel`.
  - [Latu.ML.Feature.UnivariateFeatureSelectorModel](Latu.ML.Feature.UnivariateFeatureSelectorModel.md): Attributes of a fitted `UnivariateFeatureSelectorModel`.
  - [Latu.ML.Feature.VarianceThresholdSelectorModel](Latu.ML.Feature.VarianceThresholdSelectorModel.md): Attributes of a fitted `VarianceThresholdSelectorModel`.
  - [Latu.ML.Feature.VectorIndexerModel](Latu.ML.Feature.VectorIndexerModel.md): Attributes of a fitted `VectorIndexerModel`.
  - [Latu.ML.Feature.Word2VecModel](Latu.ML.Feature.Word2VecModel.md): Attributes of a fitted `Word2VecModel`.
  - [Latu.ML.Recommendation.ALSModel](Latu.ML.Recommendation.ALSModel.md): Attributes of a fitted `ALSModel`.
  - [Latu.ML.Regression.AFTSurvivalRegressionModel](Latu.ML.Regression.AFTSurvivalRegressionModel.md): Attributes of a fitted `AFTSurvivalRegressionModel`.
  - [Latu.ML.Regression.DecisionTreeRegressionModel](Latu.ML.Regression.DecisionTreeRegressionModel.md): Attributes of a fitted `DecisionTreeRegressionModel`.
  - [Latu.ML.Regression.FMRegressionModel](Latu.ML.Regression.FMRegressionModel.md): Attributes of a fitted `FMRegressionModel`.
  - [Latu.ML.Regression.GBTRegressionModel](Latu.ML.Regression.GBTRegressionModel.md): Attributes of a fitted `GBTRegressionModel`.
  - [Latu.ML.Regression.GeneralizedLinearRegressionModel](Latu.ML.Regression.GeneralizedLinearRegressionModel.md): Attributes of a fitted `GeneralizedLinearRegressionModel`.
  - [Latu.ML.Regression.GeneralizedLinearRegressionSummary](Latu.ML.Regression.GeneralizedLinearRegressionSummary.md): Attributes of a `GeneralizedLinearRegressionSummary` — what an estimator recorded while it fitted. `Latu.ML.summary/1` is what hands you one.
  - [Latu.ML.Regression.GeneralizedLinearRegressionTrainingSummary](Latu.ML.Regression.GeneralizedLinearRegressionTrainingSummary.md): Attributes of a `GeneralizedLinearRegressionTrainingSummary` — what an estimator recorded while it fitted. `Latu.ML.summary/1` is what hands you one.
  - [Latu.ML.Regression.IsotonicRegressionModel](Latu.ML.Regression.IsotonicRegressionModel.md): Attributes of a fitted `IsotonicRegressionModel`.
  - [Latu.ML.Regression.LinearRegressionModel](Latu.ML.Regression.LinearRegressionModel.md): Attributes of a fitted `LinearRegressionModel`.
  - [Latu.ML.Regression.LinearRegressionSummary](Latu.ML.Regression.LinearRegressionSummary.md): Attributes of a `LinearRegressionSummary` — what an estimator recorded while it fitted. `Latu.ML.summary/1` is what hands you one.
  - [Latu.ML.Regression.LinearRegressionTrainingSummary](Latu.ML.Regression.LinearRegressionTrainingSummary.md): Attributes of a `LinearRegressionTrainingSummary` — what an estimator recorded while it fitted. `Latu.ML.summary/1` is what hands you one.
  - [Latu.ML.Regression.RandomForestRegressionModel](Latu.ML.Regression.RandomForestRegressionModel.md): Attributes of a fitted `RandomForestRegressionModel`.

- Plans and carriers
  - [Latu.ML.CrossValidator](Latu.ML.CrossValidator.md): K-fold cross-validation: fit every param map on every fold, and keep the best.
  - [Latu.ML.CrossValidatorModel](Latu.ML.CrossValidatorModel.md): What a cross-validation found: the winning model, and the score of every param map.
  - [Latu.ML.Estimator](Latu.ML.Estimator.md): An unfitted estimator: inert data, no session, no server-side anything.
  - [Latu.ML.Evaluator](Latu.ML.Evaluator.md): A metric, as inert data: what to measure and on which columns.
  - [Latu.ML.Helper](Latu.ML.Helper.md): An operator that is neither fitted nor applied: its work is one method on the server's own
helper object.
  - [Latu.ML.Model](Latu.ML.Model.md): A fitted model: a reference into the session's ML cache, and the context needed to use it.
  - [Latu.ML.Operator](Latu.ML.Operator.md): One operator the registry knows, as inert data.
  - [Latu.ML.Param](Latu.ML.Param.md): One parameter an operator accepts.
  - [Latu.ML.Pipeline](Latu.ML.Pipeline.md): A sequence of stages, fitted as one.
  - [Latu.ML.PipelineModel](Latu.ML.PipelineModel.md): A fitted pipeline: its stages, in order, with every estimator replaced by the model it fitted.
  - [Latu.ML.Plan](Latu.ML.Plan.md): ML commands and relations, built as protos.
  - [Latu.ML.SparseVector](Latu.ML.SparseVector.md): A Spark sparse vector, kept sparse.
  - [Latu.ML.Summary](Latu.ML.Summary.md): A model's training summary: a second server-side object, and the one the cache really drops.
  - [Latu.ML.TrainValidationSplit](Latu.ML.TrainValidationSplit.md): One split instead of k folds: fit every param map once, and keep the best.
  - [Latu.ML.TrainValidationSplitModel](Latu.ML.TrainValidationSplitModel.md): What a train/validation split found: the winning model, and one metric per param map.
  - [Latu.ML.Transformer](Latu.ML.Transformer.md): A transformer that needs no fitting — `VectorAssembler` and its kind.

- Results
  - [Latu.ML.Linalg](Latu.ML.Linalg.md): Spark's `Vector` and `Matrix`, both ways.
  - [Latu.ML.Result](Latu.ML.Result.md): Read what an `MlCommand` answered.