ML Examples
Flink ML is a library which provides machine learning (ML) APIs and infrastructures that simplify the building of ML pipelines. Users can implement ML algorithms with the standard ML APIs and further use these infrastructures to build ML pipelines for both training and inference jobs.
The Collection of Examples
https://github.com/apache/flink-ml
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├── ArrayToVectorExample.java
├── classification
│ ├── KnnExample.java
│ ├── LinearSVCExample.java
│ ├── LogisticRegressionExample.java
│ ├── NaiveBayesExample.java
│ └── OnlineLogisticRegressionExample.java
├── clustering
│ ├── AgglomerativeClusteringExample.java
│ ├── KMeansExample.java
│ └── OnlineKMeansExample.java
├── evaluation
│ └── BinaryClassificationEvaluatorExample.java
├── feature
│ ├── BinarizerExample.java
│ ├── BucketizerExample.java
│ ├── CountVectorizerExample.java
│ ├── DCTExample.java
│ ├── ElementwiseProductExample.java
│ ├── FeatureHasherExample.java
│ ├── HashingTFExample.java
│ ├── IDFExample.java
│ ├── ImputerExample.java
│ ├── IndexToStringModelExample.java
│ ├── InteractionExample.java
│ ├── KBinsDiscretizerExample.java
│ ├── MaxAbsScalerExample.java
│ ├── MinHashLSHExample.java
│ ├── MinMaxScalerExample.java
│ ├── NGramExample.java
│ ├── NormalizerExample.java
│ ├── OneHotEncoderExample.java
│ ├── OnlineStandardScalerExample.java
│ ├── PolynomialExpansionExample.java
│ ├── RandomSplitterExample.java
│ ├── RegexTokenizerExample.java
│ ├── RobustScalerExample.java
│ ├── SQLTransformerExample.java
│ ├── StandardScalerExample.java
│ ├── StopWordsRemoverExample.java
│ ├── StringIndexerExample.java
│ ├── TokenizerExample.java
│ ├── UnivariateFeatureSelectorExample.java
│ ├── VarianceThresholdSelectorExample.java
│ ├── VectorAssemblerExample.java
│ ├── VectorIndexerExample.java
│ └── VectorSlicerExample.java
├── recommendation
│ └── SwingExample.java
├── regression
│ └── LinearRegressionExample.java
├── stats
│ └── ChiSqTestExample.java
├── util
│ └── PeriodicSourceFunction.java
└── VectorToArrayExample.java
📄️ Clustering/KMeansExample
Machine Learning
📄️ Evaluation/BinaryClassificationEvaluatorExample
Machine Learning
📄️ Classification/KnnExample
Machine Learning
📄️ Features/BinarizerExample
Machine Learning