outlier detection

JD Tech Talk

Jun 12, 2025 · Product Management

How to Tackle Outliers in Internet A/B Experiments: Methods, Pitfalls, and Practical Tips

This article explores why outliers appear in large‑scale internet A/B tests, explains their impact on experiment precision, compares traditional trim and winsorize techniques, reviews a range of statistical and machine‑learning detection methods, and offers practical recommendations for handling them in product experiments.

A/B testingexperiment designoutlier detection

0 likes · 15 min read

How to Tackle Outliers in Internet A/B Experiments: Methods, Pitfalls, and Practical Tips

JD Tech

Jan 13, 2025 · Fundamentals

Handling Outliers in Internet A/B Experiments: Concepts, Methods, and Practical Recommendations

This article examines the challenges of outliers in large‑scale internet A/B testing, explains their statistical definition, outlines common causes, evaluates the benefits and limits of removal, and compares traditional trim and winsorize techniques along with practical detection and risk‑control strategies.

A/B testingTRIMdata analysis

0 likes · 8 min read

Handling Outliers in Internet A/B Experiments: Concepts, Methods, and Practical Recommendations

JD Retail Technology

Jan 7, 2025 · Fundamentals

Handling Outliers in Internet A/B Experiments: Concepts, Methods, and Practical Recommendations

The article explains why outliers destabilize internet A/B tests, outlines their causes, compares trimming and winsorizing, presents lightweight detection (e.g., kurtosis) and risk‑control strategies, and offers practical recommendations for bias‑aware removal and variance‑reduction techniques to improve experimental precision.

A/B testingBig DataTRIM

0 likes · 10 min read

Python Programming Learning Circle

May 10, 2024 · Artificial Intelligence

Comprehensive Overview of Common Anomaly Detection Methods with Code Examples

This article compiles and explains a variety of common anomaly detection techniques—including distribution‑based, distance‑based, density‑based, clustering, tree‑based, dimensionality‑reduction, classification, and prediction methods—providing algorithm descriptions, workflow steps, advantages, limitations, and ready‑to‑run Python code snippets for each approach.

Anomaly DetectionPythonmachine learning

0 likes · 23 min read

Comprehensive Overview of Common Anomaly Detection Methods with Code Examples

Model Perspective

Dec 6, 2022 · Fundamentals

How to Perform a One‑Sample t‑Test in SPSS: Step‑by‑Step Guide

This guide walks through a health‑survey example, showing how to check SPSS assumptions for outliers and normality, perform a one‑sample t‑test on BMI data, interpret the output, and draw a statistically significant conclusion about the sample mean versus the population mean.

BMISPSSnormality test

0 likes · 7 min read

How to Perform a One‑Sample t‑Test in SPSS: Step‑by‑Step Guide

NetEase LeiHuo UX Big Data Technology

Sep 5, 2022 · Artificial Intelligence

Feature Engineering in Game Data: Types, Missing Value and Outlier Handling

This article explains how feature engineering in game data involves classifying structured and unstructured, quantitative and qualitative features, and details practical methods for handling missing values and outliers to improve machine‑learning model performance.

Feature EngineeringGame Datadata preprocessing

0 likes · 9 min read

Feature Engineering in Game Data: Types, Missing Value and Outlier Handling

Model Perspective

Aug 13, 2022 · Artificial Intelligence

Mastering Outlier Detection: Techniques, Algorithms, and PyOD Implementation

Outlier detection identifies data points far from the norm, using methods such as the 3‑sigma rule, boxplots, K‑Nearest Neighbors, and numerous probabilistic and proximity‑based algorithms, with practical PyOD code examples for training, evaluating, and visualizing models across various techniques.

Anomaly Detectionmachine learningoutlier detection

0 likes · 8 min read

Mastering Outlier Detection: Techniques, Algorithms, and PyOD Implementation

Python Programming Learning Circle

Jul 15, 2022 · Artificial Intelligence

Comprehensive Overview of Common Anomaly Detection Methods with Python Code Examples

This article compiles and explains various common anomaly detection techniques—including distribution‑based, distance‑based, density‑based, clustering, tree‑based, dimensionality‑reduction, classification, and prediction methods—providing theoretical descriptions, algorithmic steps, advantages, limitations, and Python code examples for each approach.

Anomaly DetectionPythonmachine learning

0 likes · 18 min read

Comprehensive Overview of Common Anomaly Detection Methods with Python Code Examples

Python Programming Learning Circle

Feb 28, 2022 · Artificial Intelligence

Time Series Data Preprocessing: Missing Value Imputation, Denoising, and Outlier Detection

This article explains essential time series preprocessing techniques—including data sorting, handling missing values with interpolation methods, applying rolling averages, Fourier transform denoising, and detecting anomalies using rolling statistics, isolation forests, and K‑means clustering—illustrated with Python code on the AirPassengers and Google stock datasets.

Pythondata preprocessingdenoising

0 likes · 9 min read

Time Series Data Preprocessing: Missing Value Imputation, Denoising, and Outlier Detection

Python Programming Learning Circle

Dec 18, 2020 · Fundamentals

Data Exploration and Cleaning: Core Concepts, Steps, and Example Workflow

This article explains the purpose of data exploration and cleaning, outlines core analysis tasks, details missing‑value and outlier handling techniques—including various imputation methods—and illustrates the complete workflow with example images and a histogram‑based distribution analysis.

data cleaningdata explorationdata preprocessing

0 likes · 3 min read

Data Exploration and Cleaning: Core Concepts, Steps, and Example Workflow

JD Tech

Jan 26, 2018 · Artificial Intelligence

JD Big Data R&D Department Presents Three Accepted Papers at AAAI-2018

The JD Big Data R&D team announced that three of its research papers—covering cross‑domain human parsing, multi‑view outlier detection, and orthogonal weight normalization for deep neural networks—were accepted at the prestigious AAAI‑2018 conference, highlighting the department's contributions to computer vision, data mining, and deep learning.

Artificial IntelligenceCross‑domain AdaptationData Mining

0 likes · 8 min read

JD Big Data R&D Department Presents Three Accepted Papers at AAAI-2018

Architects Research Society

Jan 11, 2018 · Operations

Envoy Outlier Detection and Ejection Mechanism Overview

The article explains Envoy's outlier detection and ejection process, detailing how unhealthy upstream hosts are identified and temporarily removed based on consecutive 5xx errors, gateway failures, or success‑rate thresholds, and describes the logging format and configuration options for these health‑check mechanisms.

EnvoyHealth CheckLoad Balancing

0 likes · 6 min read

Envoy Outlier Detection and Ejection Mechanism Overview