程序包 org.apache.flink.ml.feature.robustscaler

类 RobustScaler

  • 所有已实现的接口:
    Serializable, org.apache.flink.ml.api.Estimator<RobustScaler,​RobustScalerModel>, org.apache.flink.ml.api.Stage<RobustScaler>, org.apache.flink.ml.common.param.HasInputCol<RobustScaler>, org.apache.flink.ml.common.param.HasOutputCol<RobustScaler>, org.apache.flink.ml.common.param.HasRelativeError<RobustScaler>, RobustScalerModelParams<RobustScaler>, RobustScalerParams<RobustScaler>, org.apache.flink.ml.param.WithParams<RobustScaler>
    public class RobustScaler
extends Object
implements org.apache.flink.ml.api.Estimator<RobustScaler,​RobustScalerModel>, RobustScalerParams<RobustScaler>
    An Estimator which scales features using statistics that are robust to outliers.

    This Scaler removes the median and scales the data according to the quantile range (defaults to IQR: Interquartile Range). The IQR is the range between the 1st quartile (25th quantile) and the 3rd quartile (75th quantile) but can be configured.

    Centering and scaling happen independently on each feature by computing the relevant statistics on the samples in the training set. Median and quantile range are then stored to be used on later data using the transform method.

    Standardization of a dataset is a common requirement for many machine learning estimators. Typically this is done by removing the mean and scaling to unit variance. However, outliers can often influence the sample mean / variance in a negative way. In such cases, the median and the interquartile range often give better results.

    Note that NaN values are ignored in the computation of medians and ranges.

    另请参阅:
    序列化表格