## Uses of Interface
org.apache.ignite.ml.environment.deploy.DeployableObject

- Packages that use DeployableObject
  Package | Description
  --- | ---
  org.apache.ignite.ml.clustering.gmm | Contains Gauss Mixture Model clustering algorithm (see `GmmModel`).
  org.apache.ignite.ml.clustering.kmeans | Contains kMeans clustering algorithm.
  org.apache.ignite.ml.composition | Contains classes for ensemble of models implementation.
  org.apache.ignite.ml.composition.bagging |
  org.apache.ignite.ml.composition.boosting | Contains Gradient Boosting regression and classification abstract classes allowing regressor type selecting in child classes.
  org.apache.ignite.ml.dataset.feature.extractor.impl | Package contains default implementations of `Vectorizer`.
  org.apache.ignite.ml.knn | Contains main APIs for kNN algorithms.
  org.apache.ignite.ml.knn.ann | Contains main APIs for ANN classification algorithms.
  org.apache.ignite.ml.naivebayes.compound | Contains Compound naive Bayes classifier.
  org.apache.ignite.ml.naivebayes.discrete | Contains Bernoulli naive Bayes classifier.
  org.apache.ignite.ml.naivebayes.gaussian | Contains Gaussian naive Bayes classifier.
  org.apache.ignite.ml.pipeline | Contains Pipeline API.
  org.apache.ignite.ml.preprocessing.binarization | Contains binarization preprocessor.
  org.apache.ignite.ml.preprocessing.developer | Contains Developer API preprocessors.
  org.apache.ignite.ml.preprocessing.encoding.label | Contains label encoding preprocessor.
  org.apache.ignite.ml.preprocessing.encoding.onehotencoder | Contains one hot encoding preprocessor.
  org.apache.ignite.ml.preprocessing.encoding.stringencoder | Contains string encoding preprocessor.
  org.apache.ignite.ml.preprocessing.finalization | Contains vector finalization preprocessor.
  org.apache.ignite.ml.preprocessing.imputing | Contains Imputer preprocessor.
  org.apache.ignite.ml.preprocessing.maxabsscaling | Contains Max Abs Scaler preprocessor.
  org.apache.ignite.ml.preprocessing.minmaxscaling | Contains Min Max Scaler preprocessor.
  org.apache.ignite.ml.preprocessing.normalization | Contains Normalizer preprocessor.
  org.apache.ignite.ml.preprocessing.standardscaling | Contains Standard scaler preprocessor.

- - ### Uses of DeployableObject in org.apache.ignite.ml.clustering.gmm

    Classes in org.apache.ignite.ml.clustering.gmm that implement DeployableObject
    Modifier and Type | Class and Description
    --- | ---
    `class` | `GmmModel` Gaussian Mixture Model.

  - ### Uses of DeployableObject in org.apache.ignite.ml.clustering.kmeans

    Classes in org.apache.ignite.ml.clustering.kmeans that implement DeployableObject
    Modifier and Type | Class and Description
    --- | ---
    `class` | `KMeansModel` This class encapsulates result of clusterization by KMeans algorithm.

  - ### Uses of DeployableObject in org.apache.ignite.ml.composition

    Classes in org.apache.ignite.ml.composition that implement DeployableObject
    Modifier and Type | Class and Description
    --- | ---
    `class` | `ModelsComposition` Model consisting of several models and prediction aggregation strategy.

  - ### Uses of DeployableObject in org.apache.ignite.ml.composition.bagging

    Classes in org.apache.ignite.ml.composition.bagging that implement DeployableObject
    Modifier and Type | Class and Description
    --- | ---
    `class` | `BaggedModel` This class represents model produced by `BaggedTrainer`.

  - ### Uses of DeployableObject in org.apache.ignite.ml.composition.boosting

    Classes in org.apache.ignite.ml.composition.boosting that implement DeployableObject
    Modifier and Type | Class and Description
    --- | ---
    `static class` | `GDBTrainer.GDBModel` GDB model.

  - ### Uses of DeployableObject in org.apache.ignite.ml.dataset.feature.extractor.impl

    Classes in org.apache.ignite.ml.dataset.feature.extractor.impl that implement DeployableObject
    Modifier and Type | Class and Description
    --- | ---
    `class` | `BinaryObjectVectorizer<K>` Vectorizer on binary objects.
    `class` | `DoubleArrayVectorizer<K>` Vectorizer on arrays of doubles.
    `class` | `DummyVectorizer<K>` Vectorizer on Vector.
    `class` | `LabeledDummyVectorizer<K,L>` Vectorizer on LabeledVector.
    `class` | `ObjectArrayVectorizer<K>` Vectorizer on arrays of objects.

  - ### Uses of DeployableObject in org.apache.ignite.ml.knn

    Classes in org.apache.ignite.ml.knn that implement DeployableObject
    Modifier and Type | Class and Description
    --- | ---
    `class` | `NNClassificationModel` Common methods and fields for all kNN and aNN models to predict label based on neighbours' labels.

  - ### Uses of DeployableObject in org.apache.ignite.ml.knn.ann

    Classes in org.apache.ignite.ml.knn.ann that implement DeployableObject
    Modifier and Type | Class and Description
    --- | ---
    `class` | `ANNClassificationModel` ANN model to predict labels in multi-class classification task.

  - ### Uses of DeployableObject in org.apache.ignite.ml.naivebayes.compound

    Classes in org.apache.ignite.ml.naivebayes.compound that implement DeployableObject
    Modifier and Type | Class and Description
    --- | ---
    `class` | `CompoundNaiveBayesModel` A compound Naive Bayes model which uses a composition of`GaussianNaiveBayesModel` and `DiscreteNaiveBayesModel`.

  - ### Uses of DeployableObject in org.apache.ignite.ml.naivebayes.discrete

    Classes in org.apache.ignite.ml.naivebayes.discrete that implement DeployableObject
    Modifier and Type | Class and Description
    --- | ---
    `class` | `DiscreteNaiveBayesModel` Discrete naive Bayes model which predicts result value `y` belongs to a class `C_k, k in [0..K]` as {@code p(C_k,y) =x_1*p_k1^x *...

  - ### Uses of DeployableObject in org.apache.ignite.ml.naivebayes.gaussian

    Classes in org.apache.ignite.ml.naivebayes.gaussian that implement DeployableObject
    Modifier and Type | Class and Description
    --- | ---
    `class` | `GaussianNaiveBayesModel` Simple naive Bayes model which predicts result value `y` belongs to a class `C_k, k in [0..K]` as {@code p(C_k,y) = p(C_k)*p(y_1,C_k) *...

  - ### Uses of DeployableObject in org.apache.ignite.ml.pipeline

    Classes in org.apache.ignite.ml.pipeline that implement DeployableObject
    Modifier and Type | Class and Description
    --- | ---
    `class` | `PipelineMdl<K,V>` Wraps the model produced by `Pipeline`.

  - ### Uses of DeployableObject in org.apache.ignite.ml.preprocessing.binarization

    Classes in org.apache.ignite.ml.preprocessing.binarization that implement DeployableObject
    Modifier and Type | Class and Description
    --- | ---
    `class` | `BinarizationPreprocessor<K,V>` Preprocessing function that makes binarization.

  - ### Uses of DeployableObject in org.apache.ignite.ml.preprocessing.developer

    Classes in org.apache.ignite.ml.preprocessing.developer that implement DeployableObject
    Modifier and Type | Class and Description
    --- | ---
    `class` | `MappedPreprocessor<K,V,L0,L1>` Mapped Preprocessor.
    `class` | `PatchedPreprocessor<K,V,L1,L2>` Preprocessing function that makes binarization.

  - ### Uses of DeployableObject in org.apache.ignite.ml.preprocessing.encoding.label

    Classes in org.apache.ignite.ml.preprocessing.encoding.label that implement DeployableObject
    Modifier and Type | Class and Description
    --- | ---
    `class` | `LabelEncoderPreprocessor<K,V>` Preprocessing function that makes Label encoding.

  - ### Uses of DeployableObject in org.apache.ignite.ml.preprocessing.encoding.onehotencoder

    Classes in org.apache.ignite.ml.preprocessing.encoding.onehotencoder that implement DeployableObject
    Modifier and Type | Class and Description
    --- | ---
    `class` | `OneHotEncoderPreprocessor<K,V>` Preprocessing function that makes one-hot encoding.

  - ### Uses of DeployableObject in org.apache.ignite.ml.preprocessing.encoding.stringencoder

    Classes in org.apache.ignite.ml.preprocessing.encoding.stringencoder that implement DeployableObject
    Modifier and Type | Class and Description
    --- | ---
    `class` | `StringEncoderPreprocessor<K,V>` Preprocessing function that makes String encoding.

  - ### Uses of DeployableObject in org.apache.ignite.ml.preprocessing.finalization

    Classes in org.apache.ignite.ml.preprocessing.finalization that implement DeployableObject
    Modifier and Type | Class and Description
    --- | ---
    `class` | `VectorFinalizationPreprocessor<K,V>` Preprocessing function that makes vector finalization.

  - ### Uses of DeployableObject in org.apache.ignite.ml.preprocessing.imputing

    Classes in org.apache.ignite.ml.preprocessing.imputing that implement DeployableObject
    Modifier and Type | Class and Description
    --- | ---
    `class` | `ImputerPreprocessor<K,V>` Preprocessing function that makes imputing.

  - ### Uses of DeployableObject in org.apache.ignite.ml.preprocessing.maxabsscaling

    Classes in org.apache.ignite.ml.preprocessing.maxabsscaling that implement DeployableObject
    Modifier and Type | Class and Description
    --- | ---
    `class` | `MaxAbsScalerPreprocessor<K,V>` The preprocessing function that makes maxabsscaling, transforms features to the scale `[-1,+1]`.

  - ### Uses of DeployableObject in org.apache.ignite.ml.preprocessing.minmaxscaling

    Classes in org.apache.ignite.ml.preprocessing.minmaxscaling that implement DeployableObject
    Modifier and Type | Class and Description
    --- | ---
    `class` | `MinMaxScalerPreprocessor<K,V>` Preprocessing function that makes minmaxscaling.

  - ### Uses of DeployableObject in org.apache.ignite.ml.preprocessing.normalization

    Classes in org.apache.ignite.ml.preprocessing.normalization that implement DeployableObject
    Modifier and Type | Class and Description
    --- | ---
    `class` | `NormalizationPreprocessor<K,V>` Preprocessing function that makes normalization.

  - ### Uses of DeployableObject in org.apache.ignite.ml.preprocessing.standardscaling

    Classes in org.apache.ignite.ml.preprocessing.standardscaling that implement DeployableObject
    Modifier and Type | Class and Description
    --- | ---
    `class` | `StandardScalerPreprocessor<K,V>` The preprocessing function that makes standard scaling, transforms features to make `mean` equal to `0` and `variance` equal to `1`.

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Release Date | : December 9 2020