BestHub
Sign in
Tagged articles
56 articles
Page 1 of 1
Data Party THU
Data Party THU
Nov 10, 2025 · Artificial Intelligence

Which Neural Network Method Best Estimates Uncertainty in Regression? A Comparative Study

This article examines why regression models need uncertainty estimates, explains aleatoric and epistemic uncertainty, compares four neural‑network approaches (Mean + LogStd, Mean + LogVariance, MC Dropout, simplified PPO) on a concrete‑strength dataset, and analyzes their experimental performance and limitations.

Monte Carlo DropoutPPOregression
0 likes · 10 min read
Which Neural Network Method Best Estimates Uncertainty in Regression? A Comparative Study
Alibaba Cloud Developer
Alibaba Cloud Developer
Jul 17, 2025 · Artificial Intelligence

How to Build a House Price Prediction Model with Python: A Step‑by‑Step Guide

This tutorial walks developers through the complete workflow of building a house‑price regression model—from problem definition, data collection and preprocessing, feature engineering, and model selection, to training, hyper‑parameter tuning, evaluation, optimization, deployment as a Flask service, and ongoing monitoring—using Python, pandas, scikit‑learn, and visualisation libraries.

Model DeploymentPythonfeature engineering
0 likes · 29 min read
How to Build a House Price Prediction Model with Python: A Step‑by‑Step Guide
Qborfy AI
Qborfy AI
Jul 3, 2025 · Artificial Intelligence

Why Loss Functions Matter: From Theory to Real‑World AI Applications

This article explains what loss functions are, outlines their three essential components, categorizes them for regression, classification, and generation tasks, reviews five classic loss functions with their noise resistance and gradient traits, and offers practical guidelines for selecting the right loss for AI models.

AI fundamentalsDeep Learningclassification
0 likes · 4 min read
Why Loss Functions Matter: From Theory to Real‑World AI Applications
Qunhe Technology Quality Tech
Qunhe Technology Quality Tech
Jun 12, 2025 · Artificial Intelligence

Boosting CAD & Ad Design Algorithms with a Goldenset Review Platform

The article describes how a custom algorithm review platform, built around goldenset test cases, quantifies and visualizes CAD recognition and advertising design tool outputs, enabling rapid regression testing, objective metric tracking, and efficient manual review, ultimately improving development speed and bug detection rates.

AdvertisingCADMetrics
0 likes · 12 min read
Boosting CAD & Ad Design Algorithms with a Goldenset Review Platform
AI Cyberspace
AI Cyberspace
Apr 5, 2025 · Fundamentals

Mastering Statistics: From Data Basics to Regression Analysis

This comprehensive guide explains the fundamentals of statistics—including data types, collection, descriptive analysis, visualization tools, measures of central tendency and dispersion, correlation techniques, and regression modeling—providing practical insights for data scientists and engineers seeking to extract meaningful insights from data.

correlationdata analysisdescriptive statistics
0 likes · 20 min read
Mastering Statistics: From Data Basics to Regression Analysis
FunTester
FunTester
Sep 11, 2024 · Operations

Pinterest Performance Plan: Real‑User Monitoring, Regression Detection, and Alerting

Pinterest’s performance program details how the team defines custom Pinner Wait Time metrics, uses real‑user monitoring and fine‑grained alerts to detect regressions quickly, and follows structured root‑cause analysis and ownership processes to prevent performance degradation across web surfaces.

Operationsmonitoringreal‑user
0 likes · 18 min read
Pinterest Performance Plan: Real‑User Monitoring, Regression Detection, and Alerting
转转QA
转转QA
Jun 20, 2024 · Operations

Implementation Plan and Results of API Automation Testing

This article outlines the background, step‑by‑step implementation plan, execution mechanisms, sustainable maintenance practices, and measurable outcomes of introducing API automation testing to ensure high‑quality iterative development and stable online services.

API testingcontinuous integrationquality assurance
0 likes · 10 min read
Implementation Plan and Results of API Automation Testing
Sohu Tech Products
Sohu Tech Products
Mar 6, 2024 · Artificial Intelligence

Mastering Regression: A Comprehensive Guide to Linear and Non‑Linear Models

This article provides an in‑depth overview of regression prediction, covering linear models like OLS, Lasso, Ridge, and Bayesian approaches, as well as non‑linear techniques such as tree ensembles, SVR, KNN, neural networks, and advanced deep learning frameworks for tabular data.

Deep Learninggradient boostinglinear models
0 likes · 13 min read
Mastering Regression: A Comprehensive Guide to Linear and Non‑Linear Models
IT Services Circle
IT Services Circle
Mar 6, 2024 · Artificial Intelligence

Comprehensive Overview of Ten Regression Algorithms with Core Concepts and Code Examples

This article provides a comprehensive summary of ten regression algorithms—including linear, ridge, Lasso, decision tree, random forest, gradient boosting, SVR, XGBoost, LightGBM, and neural network regression—detailing their principles, advantages, disadvantages, suitable scenarios, and offering core Python code examples for each.

Pythongradient boostingmachine learning
0 likes · 33 min read
Comprehensive Overview of Ten Regression Algorithms with Core Concepts and Code Examples
Test Development Learning Exchange
Test Development Learning Exchange
Oct 19, 2023 · Artificial Intelligence

Common Machine Learning Algorithms for Data Prediction with Python Code Examples

This article introduces ten widely used machine learning algorithms for data prediction, explains their core concepts, and provides complete Python code snippets using scikit‑learn and related libraries to help readers implement regression, classification, and time‑series forecasting tasks.

Pythonclassificationdata prediction
0 likes · 12 min read
Common Machine Learning Algorithms for Data Prediction with Python Code Examples
Model Perspective
Model Perspective
Oct 9, 2023 · Fundamentals

Unpacking Gender Wage Gaps: Oaxaca‑Blinder, Regression & Simulated Data

This article reviews Claudia Goldin’s Nobel‑winning research on gender wage disparities, explaining the Oaxaca‑Blinder decomposition, multiple linear regression, and mean‑difference models, and demonstrates their application with a synthetic dataset and Python code to illustrate how education, experience, and gender affect wages.

Oaxaca-Blindergender wage gaplabor economics
0 likes · 10 min read
Unpacking Gender Wage Gaps: Oaxaca‑Blinder, Regression & Simulated Data
Rare Earth Juejin Tech Community
Rare Earth Juejin Tech Community
Sep 15, 2023 · Artificial Intelligence

Understanding Machine Learning vs Deep Learning and a Practical sklearn Regression Tutorial

This article explains the difference between machine learning and deep learning, compares ML algorithms with traditional logic code, introduces the scikit‑learn library, demonstrates data preprocessing, model training with RandomForestRegressor, and shows how to build a voting regressor for disease progression prediction using Python.

Pythonmachine learningregression
0 likes · 18 min read
Understanding Machine Learning vs Deep Learning and a Practical sklearn Regression Tutorial
Python Programming Learning Circle
Python Programming Learning Circle
Jun 12, 2023 · Artificial Intelligence

10 Common Loss Functions and Their Python Implementations

This article explains ten widely used loss functions for regression and classification tasks, describes their mathematical definitions, compares their purposes, and provides complete Python code examples for each, helping readers understand how to select and implement appropriate loss metrics in machine‑learning models.

Loss Functionsaiclassification
0 likes · 10 min read
10 Common Loss Functions and Their Python Implementations
Python Programming Learning Circle
Python Programming Learning Circle
May 26, 2023 · Fundamentals

Introduction to Statsmodels: Installation, Data Loading, and Basic Statistical Analysis with Python

This article introduces the Python Statsmodels library, explains its key features such as linear regression, GLM, time‑series and robust methods, shows how to install it, load data with pandas, perform descriptive statistics, visualizations, hypothesis testing, and simple and multiple linear regression examples.

PythonStatistical ModelingStatsmodels
0 likes · 6 min read
Introduction to Statsmodels: Installation, Data Loading, and Basic Statistical Analysis with Python
Model Perspective
Model Perspective
Jan 13, 2023 · Artificial Intelligence

Master Classic Modeling with Python: LP, Graphs, Clustering, PCA & More

This article presents Python implementations of classic mathematical modeling techniques—including linear programming with PuLP, shortest‑path analysis using NetworkX, K‑means and hierarchical clustering, principal component analysis, frequent‑pattern mining with FP‑Growth, and linear regression and K‑nearest‑neighbors—providing code snippets, explanations, and visualizations to guide readers through each method.

Frequent Pattern MiningPCAPython
0 likes · 12 min read
Master Classic Modeling with Python: LP, Graphs, Clustering, PCA & More
Model Perspective
Model Perspective
Sep 14, 2022 · Fundamentals

Mastering Grouped and Dummy Variable Regression: Weighted Models Explained

This article explains how regression can handle grouped (aggregated) data using weighted least squares, illustrates the impact of heteroskedasticity, and shows how dummy variables encode categorical factors for flexible, non‑parametric modeling of treatment effects.

Dummy VariablesStatistical Modelinggrouped data
0 likes · 12 min read
Mastering Grouped and Dummy Variable Regression: Weighted Models Explained
Model Perspective
Model Perspective
Sep 10, 2022 · Fundamentals

What Is Statistics? A Beginner’s Guide to Data Collection, Analysis, and Inference

This article introduces the fundamentals of statistics, covering its purpose, types, data collection methods, data organization steps, graphical representation, measures of central tendency and dispersion, probability concepts, parameter estimation, hypothesis testing, and the distinction between correlation and regression analysis.

data analysishypothesis testingprobability
0 likes · 11 min read
What Is Statistics? A Beginner’s Guide to Data Collection, Analysis, and Inference
Model Perspective
Model Perspective
Sep 6, 2022 · Fundamentals

How Link Functions Extend Linear Regression to Generalized Models

This article explains how the traditional linear regression assumption can be relaxed by using link functions to transform nonlinear outputs into linear responses, enabling more flexible generalized linear models for probabilities and count data.

Modelinggeneralized linear modellink function
0 likes · 1 min read
How Link Functions Extend Linear Regression to Generalized Models
Model Perspective
Model Perspective
Aug 25, 2022 · Artificial Intelligence

Mastering Regression: Key Assumptions, Metrics, and Model Evaluation

This article explains the fundamental assumptions of linear regression, compares linear and nonlinear models, discusses multicollinearity, outliers, regularization, heteroscedasticity, VIF, stepwise regression, and reviews essential evaluation metrics such as MAE, MSE, RMSE, R² and Adjusted R².

MetricsModel Evaluationlinear regression
0 likes · 12 min read
Mastering Regression: Key Assumptions, Metrics, and Model Evaluation
Model Perspective
Model Perspective
Aug 16, 2022 · Fundamentals

13 Essential Statistical Analysis Methods Every Researcher Should Know

This article outlines thirteen key statistical techniques—including descriptive and inferential methods, hypothesis testing, reliability analysis, contingency tables, regression, clustering, discriminant, factor, and time‑series analysis—explaining their purposes, assumptions, and typical applications for researchers and data analysts.

clusteringdata analysishypothesis testing
0 likes · 25 min read
13 Essential Statistical Analysis Methods Every Researcher Should Know
Model Perspective
Model Perspective
Aug 8, 2022 · Artificial Intelligence

Mastering sklearn.svm: Parameters, Grid Search, and Real-World Examples

An in‑depth guide to sklearn.svm explains SVM classification and regression, details key parameters such as C and kernel types, demonstrates how to use GridSearchCV for hyperparameter tuning, and provides complete Python code examples for iris classification and California housing price prediction.

GridSearchCVPythonmachine learning
0 likes · 6 min read
Mastering sklearn.svm: Parameters, Grid Search, and Real-World Examples
Model Perspective
Model Perspective
Aug 3, 2022 · Artificial Intelligence

Explore the Most Popular Machine Learning Algorithms: A Comprehensive Guide

This article provides a thorough overview of the most widely used machine learning algorithms, classifying them by learning style and problem type, and highlighting popular methods such as supervised, unsupervised, semi‑supervised, regression, instance‑based, regularization, decision‑tree, Bayesian, clustering, association rule, neural network, deep learning, dimensionality‑reduction, and ensemble techniques.

AlgorithmsDeep LearningUnsupervised Learning
0 likes · 10 min read
Explore the Most Popular Machine Learning Algorithms: A Comprehensive Guide
Model Perspective
Model Perspective
Jul 23, 2022 · Artificial Intelligence

LASSO Regression Explained: Theory, Case Studies, and Python Code

This article introduces the mathematical foundations of ordinary least squares, ridge, and LASSO regression, explains why LASSO requires coordinate descent, presents two real-world case studies with data, and provides complete Python code for fitting, visualizing, and interpreting LASSO models.

LASSOPythonmachine learning
0 likes · 8 min read
LASSO Regression Explained: Theory, Case Studies, and Python Code
NetEase Yanxuan Technology Product Team
NetEase Yanxuan Technology Product Team
Jul 4, 2022 · Big Data

Data Indicator Testing Platform and Quality Assurance

The article presents an Indicator Testing Platform that automates metric validation—covering timeliness, completeness, accuracy, and consistency—through model‑level comparison, regression, online monitoring, and TDD‑style testing, dramatically reducing manual effort and enabling rapid detection and correction of data quality issues across thousands of business indicators.

Automated TestingData PlatformData Quality
0 likes · 10 min read
Data Indicator Testing Platform and Quality Assurance
Model Perspective
Model Perspective
Jul 3, 2022 · Fundamentals

Explore 20+ Essential Modeling Articles: From Differential Equations to Machine Learning

This curated list groups recent articles on change and predictive models, covering topics such as war dynamics, population, epidemic spread, differential equations, regression, time‑series analysis, machine learning classifiers, and grey‑prediction techniques, providing students with ready references for diverse modeling approaches.

ModelingTime Seriesmachine learning
0 likes · 3 min read
Explore 20+ Essential Modeling Articles: From Differential Equations to Machine Learning
Model Perspective
Model Perspective
Jun 17, 2022 · Artificial Intelligence

Understanding Supervised Learning: Regression vs Classification Explained

This article explains the fundamentals of supervised machine learning, distinguishing between regression and classification, describing how algorithms learn mappings from inputs to outputs, and outlining common models such as linear regression, logistic regression, decision trees, SVMs, random forests, and neural networks.

Artificial Intelligenceclassificationmachine learning
0 likes · 4 min read
Understanding Supervised Learning: Regression vs Classification Explained
Python Programming Learning Circle
Python Programming Learning Circle
May 10, 2022 · Artificial Intelligence

Seven Classic Regression Models for Machine Learning

This article introduces regression analysis and explains why it is essential for predictive modeling, then details seven widely used regression techniques—including linear, logistic, polynomial, stepwise, ridge, lasso, and elastic‑net—while offering guidance on selecting the most appropriate model for a given dataset.

Model Selectionlasso regressionlinear regression
0 likes · 13 min read
Seven Classic Regression Models for Machine Learning
Code DAO
Code DAO
Dec 18, 2021 · Artificial Intelligence

Implement Random Forest Regression in Python using Scikit-Learn

This article explains the fundamentals of random forest regression, describes why it outperforms single decision trees for nonlinear or noisy data, defines bootstrapping and bagging, and provides a step‑by‑step Python example using NumPy, Pandas, and Scikit‑Learn’s RandomForestRegressor with data loading, preprocessing, model training, prediction, and evaluation via MSE and R².

BootstrappingPythonRandom Forest
0 likes · 6 min read
Implement Random Forest Regression in Python using Scikit-Learn
Code DAO
Code DAO
Dec 18, 2021 · Artificial Intelligence

Accelerating Gradient Boosting with CatBoost

This article explains how CatBoost implements gradient boosting, handles categorical features without preprocessing, lists its key advantages, details common training parameters, and provides a step‑by‑step regression example with code for fitting, cross‑validation, grid search, tree visualization, and parameter inspection.

CatBoostgradient boostinghyperparameter tuning
0 likes · 7 min read
Accelerating Gradient Boosting with CatBoost
Code DAO
Code DAO
Dec 16, 2021 · Fundamentals

How Poisson Hidden Markov Models Enable Count‑Based Time‑Series Regression

This article explains how mixing a Poisson process with a discrete k‑state hidden Markov model creates a Poisson HMM that captures autocorrelation in integer‑valued time‑series, detailing the model formulation, prediction via expectation over states, and parameter estimation using MLE or EM.

EMMLEMarkov model
0 likes · 11 min read
How Poisson Hidden Markov Models Enable Count‑Based Time‑Series Regression
Code DAO
Code DAO
Dec 12, 2021 · Artificial Intelligence

How to Boost Text Analysis Accuracy on a 2‑Billion‑Word Corpus

This article explains practical techniques for improving NLP model accuracy on massive corpora, covering challenges of multi‑field text, word‑embedding choices, a fasttext‑based regression demo with book‑review data, feature engineering tricks, and a comparison with tf‑idf + LASSO.

NLPPythonWord2Vec
0 likes · 13 min read
How to Boost Text Analysis Accuracy on a 2‑Billion‑Word Corpus
Python Programming Learning Circle
Python Programming Learning Circle
Jun 19, 2021 · Artificial Intelligence

Template Notebook for Building Machine Learning Models with Scikit-learn

This notebook provides ready‑to‑use Python code templates for ten common machine‑learning algorithms—including linear regression, logistic regression, decision trees, Naïve Bayes, SVM, K‑Nearest Neighbors, K‑Means, Random Forest, PCA, and Gradient Boosting—showing how to import, train, evaluate, and predict with scikit‑learn.

aiclassificationmachine learning
0 likes · 8 min read
Template Notebook for Building Machine Learning Models with Scikit-learn
DeWu Technology
DeWu Technology
Mar 4, 2021 · Fundamentals

Dominance Analysis for Attribution in Data Analytics

The article explains that attribution analysis of metric declines requires a quantitative approach, introducing Dominance Analysis—a econometric technique that decomposes regression R² into variable-specific contributions by fitting all subset models, averaging marginal effects, ranking factors, and providing a Python implementation with the dominance‑analysis package illustrated on the Boston Housing dataset.

Data AnalyticsStatistical Modelingattribution
0 likes · 7 min read
Dominance Analysis for Attribution in Data Analytics
Youzan Coder
Youzan Coder
Oct 14, 2020 · Operations

How Youzan’s Testing Team Supercharged API Automation for Faster, More Reliable Releases

This article details Youzan’s testing team’s evolving API automation strategy, covering architectural context, dual‑approach request simulation, regression efficiency enhancements, blind‑spot elimination through online validation and traffic recording‑replay, and practical lessons for large‑scale product delivery.

API testingDubboMicroservices
0 likes · 11 min read
How Youzan’s Testing Team Supercharged API Automation for Faster, More Reliable Releases
Didi Tech
Didi Tech
Sep 17, 2020 · Artificial Intelligence

Machine Learning Practices in DiDi's Network Positioning: From Unsupervised Probabilistic Models to End‑to‑End CNN

DiDi’s network‑positioning system, which serves billions of daily location requests using Wi‑Fi and cellular signals, has evolved from an unsupervised probabilistic fingerprint matcher through a supervised GBDT‑DeepFM regression model to a fully end‑to‑end CNN that directly predicts coordinates, delivering markedly higher accuracy.

CNNLBSnetwork positioning
0 likes · 11 min read
Machine Learning Practices in DiDi's Network Positioning: From Unsupervised Probabilistic Models to End‑to‑End CNN
DataFunTalk
DataFunTalk
Aug 14, 2020 · Artificial Intelligence

Illustrated Guide to the Complete Machine Learning Workflow

This article presents a hand‑drawn, illustrated walkthrough of the entire machine‑learning pipeline—from dataset definition, exploratory data analysis, preprocessing, and data splitting to model building, algorithm selection, hyper‑parameter tuning, feature selection, and evaluation for both classification and regression tasks.

Model Evaluationclassificationcross-validation
0 likes · 17 min read
Illustrated Guide to the Complete Machine Learning Workflow
Youku Technology
Youku Technology
Jul 24, 2020 · Backend Development

Hot Link Coverage and Comparative Testing for Backend Services

The article describes Alibaba’s flow‑based comparative testing and introduces Youku’s hot‑link coverage technique, which instruments method chains to automatically identify high‑frequency request paths, recommend them for regression testing, achieve full business‑link coverage without manual effort, and outlines future machine‑learning enhancements.

coveragehot-linkmethod-chain
0 likes · 11 min read
Hot Link Coverage and Comparative Testing for Backend Services
DataFunTalk
DataFunTalk
May 29, 2019 · Artificial Intelligence

A Comprehensive Overview of Statistical Learning Methods for Machine Learning Interview Preparation

This article provides a detailed, English-language summary of key statistical learning concepts—including perceptron, k‑nearest neighbors, Naive Bayes, decision trees, logistic regression, support vector machines, boosting, EM, HMM, neural networks, K‑Means, bagging, Apriori and dimensionality reduction—complete with formulas, algorithm steps, and illustrative diagrams to aid interview preparation.

Neural NetworksSupport Vector Machineclassification
0 likes · 44 min read
A Comprehensive Overview of Statistical Learning Methods for Machine Learning Interview Preparation
37 Interactive Technology Team
37 Interactive Technology Team
Apr 28, 2019 · Artificial Intelligence

Application of Machine Learning Algorithms in Mobile Game Recharge Monitoring

By applying XGBoost‑based regression models that are retrained daily on two‑week order data and tuned per sub‑package, the mobile‑game recharge monitoring system predicts 10‑minute order volumes, sharply cuts false alarms from hundreds to dozens, and delivers precise, scalable alerts for game operations.

Mobile GamingModel EvaluationXGBoost
0 likes · 8 min read
Application of Machine Learning Algorithms in Mobile Game Recharge Monitoring
Qunar Tech Salon
Qunar Tech Salon
Oct 9, 2018 · Artificial Intelligence

Ridge Regression with scikit-learn: Theory, Implementation, and Example

This article introduces Ridge regression, explains its theory and regularization role, discusses overfitting and bias‑variance trade‑offs, presents scikit‑learn parameters, and provides a complete Python example from data loading to model training, evaluation, and optimal alpha selection.

PythonRegularizationmachine learning
0 likes · 7 min read
Ridge Regression with scikit-learn: Theory, Implementation, and Example
Architects' Tech Alliance
Architects' Tech Alliance
Mar 9, 2018 · Artificial Intelligence

Master Machine Learning Basics: From PCA to KNN Explained with Visual Demos

An in‑depth, visual guide walks readers through the fundamentals of machine learning—distinguishing supervised from unsupervised approaches, explaining dimensionality reduction with PCA, detailing clustering techniques such as hierarchical clustering, K‑Means and DBSCAN, and summarizing core regression and classification algorithms including linear regression, SVM, decision trees, logistic regression, Naïve Bayes, and KNN.

Unsupervised Learningclassificationclustering
0 likes · 11 min read
Master Machine Learning Basics: From PCA to KNN Explained with Visual Demos
Architecture Digest
Architecture Digest
Feb 13, 2018 · Artificial Intelligence

Overview of Common Machine Learning Models: Characteristics, Advantages, and Disadvantages

This article provides a concise overview of fifteen widely used machine learning models—including decision trees, random forests, k‑means, KNN, EM, linear and logistic regression, Naive Bayes, Apriori, Boosting, GBDT, SVM, neural networks, HMM, and CRF—detailing their features, strengths, weaknesses, and typical application scenarios.

Neural Networksclassificationclustering
0 likes · 12 min read
Overview of Common Machine Learning Models: Characteristics, Advantages, and Disadvantages
Meituan Technology Team
Meituan Technology Team
Nov 23, 2017 · Artificial Intelligence

Improving Food Delivery ETA Prediction with GBDT Feature Construction

By training Gradient Boosted Decision Trees on offline delivery data, extracting leaf‑node indices as one‑hot features, and merging them with merchant, traffic, and weather information, the study reduces overall ETA MAE by 3.4%, raises N‑minute accuracy by 2.2 points, and achieves larger gains for high‑value orders, demonstrating that GBDT‑derived features markedly improve food‑delivery time predictions.

ETAGBDTfood delivery
0 likes · 13 min read
Improving Food Delivery ETA Prediction with GBDT Feature Construction
Huawei Cloud Developer Alliance
Huawei Cloud Developer Alliance
Oct 12, 2017 · Artificial Intelligence

Which Machine Learning Skills Will Be Most In‑Demand in the Next 3‑5 Years?

The article explains that industrial AI needs specialists who can apply machine‑learning models to specific domains, outlines essential fundamentals such as regression, classification, neural networks, data visualization, and unsupervised learning, and offers practical career advice for students and early‑career professionals seeking to transition into machine‑learning roles.

Data visualizationIndustrial AINeural Networks
0 likes · 11 min read
Which Machine Learning Skills Will Be Most In‑Demand in the Next 3‑5 Years?
ITPUB
ITPUB
Sep 18, 2017 · Databases

Why MySQL 5.7 Partition Tables Slow Down: Uncovering an InnoDB Lock Regression

The article investigates a performance regression in MySQL 5.7.18 where partitioned tables cause excessive InnoDB row locking, leading to lock timeouts and slower updates, explains the root cause through source‑code analysis, reproduces the issue, validates the findings, and confirms it as a MySQL bug.

InnoDBPartition Tableslocking
0 likes · 10 min read
Why MySQL 5.7 Partition Tables Slow Down: Uncovering an InnoDB Lock Regression
Ctrip Technology
Ctrip Technology
Sep 4, 2017 · Databases

Investigation of Performance Degradation and Locking Issues in MySQL 5.7.18 Partition Tables

This article investigates the performance degradation and lock timeout issues observed in MySQL 5.7.18 partition tables, reproduces the problem with test cases, analyzes InnoDB lock behavior through source code debugging, validates the root cause related to partition scan locking, and confirms it as a regression bug in MySQL 5.7.

InnoDBPartition Tablesdatabase
0 likes · 8 min read
Investigation of Performance Degradation and Locking Issues in MySQL 5.7.18 Partition Tables
MaGe Linux Operations
MaGe Linux Operations
May 7, 2017 · Artificial Intelligence

Big Data & Machine Learning: Core Definitions and Essential Algorithms

This article explains what big data and machine learning are, their interrelationship, various big‑data analysis approaches, core machine‑learning concepts, and details ten fundamental algorithms—including regression, neural networks, SVM, clustering, dimensionality reduction, and recommendation—while highlighting their roles in modern data‑driven applications.

Big DataNeural Networksclustering
0 likes · 24 min read
Big Data & Machine Learning: Core Definitions and Essential Algorithms
360 Zhihui Cloud Developer
360 Zhihui Cloud Developer
May 4, 2017 · Artificial Intelligence

Master Linear, Weighted, and Ridge Regression: Theory, Code, and Evaluation

This article introduces regression concepts, explains linear, locally weighted, and ridge regression methods, demonstrates their mathematical foundations, provides Python implementations, and discusses model evaluation techniques to help readers choose the appropriate regression approach for their data.

linear regressionmachine learningregression
0 likes · 10 min read
Master Linear, Weighted, and Ridge Regression: Theory, Code, and Evaluation