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Dec 1, 2023 · Number= {UCB/EECS-2023-259}, Abstract= {The design of statistical estimators robust to outliers has been a mainstay of statistical research through the past six decades. These techniques are even more prescient in the contemporary landscape where large-scale machine learning systems are deployed in increasingly noisy and adaptive environments.
Aug 14, 2018 · Robust does not mean immune, or invulnerable, and the purpose of scaling is not to "remove" outliers and extreme values - this is a separate task with its own methodologies; this is again clearly mentioned in the relevant scikit-learn docs:
Apr 11, 2012 · 2. I've been reading/looking around for literature on support vector regressions that are relatively robust to outliers. I understand that standard SVRs can be significantly influenced by a few large outliers. From what I've read (and I'm no academic to be sure), there appears to be a number of different approaches.
class sklearn.preprocessing.RobustScaler(*, with_centering=True, with_scaling=True, quantile_range=(25.0, 75.0), copy=True, unit_variance=False) [source] #. Scale 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).
Sep 21, 2023 · The box plot uses inter-quartile range to detect outliers. Here, we first determine the quartiles Q 1 and Q 3. Interquartile range is given by, IQR = Q3 — Q1. Upper limit = Q3+1.5*IQR. Lower limit = Q1–1.5*IQR. Anything below the lower limit and above the upper limit is considered an outlier.
Jan 30, 2024 · Symmetric Distributions Without Outliers: The mean and standard deviation are typically sufficient. Skewed Distributions or Presence of Outliers: Median, trimmed mean, or Winsorized mean are more reliable. The median is highly robust but less sensitive to small changes in data, making it suitable for highly skewed distributions.
Mar 25, 2024 · Although many researchers attempt to identify outliers with measures based on the mean (e.g., z scores), those methods can be problematic. This is because the mean and standard deviation themselves are not robust to the influence of outliers and those methods also assume normally distributed data (i.e., a Gaussian distribution).
