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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.

CADadvertisingalgorithm

0 likes · 12 min read

Boosting CAD & Ad Design Algorithms with a Goldenset Review Platform

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.

MonitoringOperationsPerformance

0 likes · 18 min read

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

Model Perspective

Jul 16, 2024 · Fundamentals

Does Workplace Environment Boost Performance? A Practical Mediation Analysis Walkthrough

Using a real‑world example of office conditions, this article explains the concept of mediation effects, outlines the three‑step regression method, and demonstrates Sobel and Bootstrap tests to determine whether workplace environment influences employee performance through job satisfaction.

Performancejob satisfactionmediation analysis

0 likes · 8 min read

Does Workplace Environment Boost Performance? A Practical Mediation Analysis Walkthrough

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 testingAutomationContinuous Integration

0 likes · 10 min read

Implementation Plan and Results of API Automation Testing

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.

Gradient BoostingPythonmachine learning

0 likes · 33 min read

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

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

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

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.

Pythondeep learningmachine learning

0 likes · 18 min read

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

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.

AIclassificationloss functions

0 likes · 10 min read

10 Common Loss Functions and Their Python Implementations

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.

Pythondata-analysisregression

0 likes · 6 min read

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

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.

ClusteringFrequent Pattern MiningOptimization

0 likes · 12 min read

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

Model Perspective

Nov 8, 2022 · Fundamentals

Explore Key Statistical & Probabilistic Model Articles – A Curated Guide

This article compiles recent Chinese-language resources on statistical and probabilistic models, organizing links to tutorials covering clustering analysis, various regression techniques, and causal inference, helping readers quickly locate essential learning material.

Clusteringcausal analysismachine learning

0 likes · 3 min read

Explore Key Statistical & Probabilistic Model Articles – A Curated Guide

Model Perspective

Nov 4, 2022 · Fundamentals

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

This curated list groups recent articles on change and prediction models, covering topics such as war dynamics, population, infectious diseases, differential equations, regression, time‑series analysis, and various machine‑learning techniques, providing quick access to each resource.

differential equationsmachine learningmodeling

0 likes · 3 min read

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

Model Perspective

Oct 9, 2022 · Artificial Intelligence

Mastering AdaBoost: How Boosting Turns Weak Learners into Strong Models

This article provides a comprehensive overview of the AdaBoost algorithm, explaining its boosting principles, how it computes error rates, determines weak learner weights, updates sample weights, and combines classifiers for both classification and regression tasks, while also covering loss‑function optimization, regularization, and practical advantages and drawbacks.

AdaBoostBoostingclassification

0 likes · 9 min read

Mastering AdaBoost: How Boosting Turns Weak Learners into Strong Models

Model Perspective

Oct 1, 2022 · Artificial Intelligence

Boost Your Models with LightGBM: Fast, Accurate Gradient Boosting in Python

This article introduces LightGBM, a high‑performance gradient boosting framework, explains its advantages over XGBoost, and provides step‑by‑step Python code for building classification and regression models on the Iris dataset, including model training, evaluation, and visualizing feature importance and tree structures.

Gradient BoostingLightGBMPython

0 likes · 5 min read

Boost Your Models with LightGBM: Fast, Accurate Gradient Boosting in Python

Model Perspective

Sep 27, 2022 · Artificial Intelligence

Master XGBoost: Boosting Trees Explained with Python Code

This article explains the core concepts of XGBoost as a boosting tree algorithm, describes how it builds ensembles of decision trees to predict outcomes, and provides complete Python implementations for classification and regression using the Scikit-learn interface, along with visualizations of trees and feature importance.

BoostingPythonXGBoost

0 likes · 4 min read

Master XGBoost: Boosting Trees Explained with Python Code

Model Perspective

Sep 16, 2022 · Fundamentals

Why Adding Non‑Confounding Controls Can Boost Causal Estimates (And When They Hurt)

This article explains how adding covariates that are not confounders can reduce outcome variance and improve causal inference, while controlling for variables that only predict treatment may introduce selection bias and inflate estimation error.

Variance Reductioncausal inferencecontrol variables

0 likes · 21 min read

Why Adding Non‑Confounding Controls Can Boost Causal Estimates (And When They Hurt)

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 variablesgrouped dataheteroskedasticity

0 likes · 12 min read

Mastering Grouped and Dummy Variable Regression: Weighted Models Explained

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

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.

generalized linear modellink functionmodeling

0 likes · 1 min read

How Link Functions Extend Linear Regression to Generalized Models