RobustScaler

Target entity: RobustScaler
Context triple: [StandardScaler, relatedTo, RobustScaler]

• A. StandardScaler
  StandardScaler is a preprocessing tool in machine learning that normalizes numerical features by removing the mean and scaling to unit variance.
• B. Box–Cox transformation
  The Box–Cox transformation is a family of power transformations used in statistics to stabilize variance and make data more normally distributed for modeling and analysis.
• C. Huber M-estimator
  The Huber M-estimator is a robust statistical estimator of location that combines properties of the mean and median by using a loss function that is quadratic for small residuals and linear for large ones to reduce the influence of outliers.
• D. Tukey's biweight
  Tukey's biweight is a robust statistical estimator that downweights outliers to provide resistant measures of central tendency or regression fits.
• E. Instance Normalization
  Instance Normalization is a neural network normalization technique that normalizes each individual sample and channel independently, commonly used in tasks like style transfer to stabilize training and control feature statistics.
• F. None of above. chosen
• G. Unsure - the case is ambiguous/there is not enough information to decide.

Target entity: RobustScaler
Target entity description: RobustScaler is a data preprocessing tool in machine learning that scales features using statistics robust to outliers, such as the median and interquartile range.

• A. StandardScaler
  StandardScaler is a preprocessing tool in machine learning that normalizes numerical features by removing the mean and scaling to unit variance.
• B. Box–Cox transformation
  The Box–Cox transformation is a family of power transformations used in statistics to stabilize variance and make data more normally distributed for modeling and analysis.
• C. Huber M-estimator
  The Huber M-estimator is a robust statistical estimator of location that combines properties of the mean and median by using a loss function that is quadratic for small residuals and linear for large ones to reduce the influence of outliers.
• D. Tukey's biweight
  Tukey's biweight is a robust statistical estimator that downweights outliers to provide resistant measures of central tendency or regression fits.
• E. Instance Normalization
  Instance Normalization is a neural network normalization technique that normalizes each individual sample and channel independently, commonly used in tasks like style transfer to stabilize training and control feature statistics.
• F. None of above. chosen