Examples
Evaluation/BinaryClassificationEvaluatorExample Binary Classification Evaluator calculates the evaluation metrics for binary classification. The input data has rawPrediction, label, and an optional weight column. The rawPrediction can be of type double (binary 0/1 prediction, or probability of label 1) or of type vector (length-2 vector of raw predictions, scores, or label probabilities). The output may contain different metrics defined by the parameter MetricsNames. | Clustering/KMeansExample 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. |
Classification/KnnExample K Nearest Neighbor(KNN) is a classification algorithm. The basic assumption of KNN is that if most of the nearest K neighbors of the provided sample belong to the same label, then it is highly probable that the provided sample also belongs to that label. | Features/BinarizerExample Binarizer binarizes the columns of continuous features by the given thresholds. The continuous features may be DenseVector, SparseVector, or Numerical Value. |