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Alibaba Cloud Developer
Alibaba Cloud Developer
Feb 2, 2026 · Artificial Intelligence

Boosting A/B Experiment Automation: Prompt Engineering Achieves 80% Accuracy

This article details how a production‑grade prompt system powered by large language models was designed to replace manual A/B experiment inspection, introducing a six‑level priority decision tree, robust data preprocessing, and systematic bad‑case analysis that lifted automation accuracy from 68% to over 80% while providing clear, explainable recommendations.

A/B testingLLMPrompt engineering
0 likes · 46 min read
Boosting A/B Experiment Automation: Prompt Engineering Achieves 80% Accuracy
AI Code to Success
AI Code to Success
Feb 27, 2025 · Artificial Intelligence

Master Decision Trees: Theory, Construction, and Python Implementation

This article provides a comprehensive guide to decision tree algorithms, covering their theoretical foundations, key components, construction workflow—including data preprocessing, feature selection, tree growth, stopping criteria, and pruning—followed by an overview of popular variants like ID3, C4.5, CART, practical advantages, applications, and a complete Python implementation using scikit-learn.

Pythonclassificationdata preprocessing
0 likes · 29 min read
Master Decision Trees: Theory, Construction, and Python Implementation
DaTaobao Tech
DaTaobao Tech
Mar 4, 2024 · Artificial Intelligence

Iris Classification with Machine Learning: Data Exploration and Classic Algorithms

This beginner-friendly guide walks through loading the classic Iris dataset, performing exploratory data analysis, and implementing four fundamental classifiers—Decision Tree, Logistic Regression, Support Vector Machine, and K‑Nearest Neighbors—complete with training, visualization, and accuracy evaluation, illustrating a full machine‑learning workflow.

classificationdecision treeiris dataset
0 likes · 22 min read
Iris Classification with Machine Learning: Data Exploration and Classic Algorithms
Model Perspective
Model Perspective
Jul 30, 2022 · Artificial Intelligence

How Decision Trees Predict House Locations: From Intuition to Overfitting

This article explains machine learning fundamentals using a house‑location classification example, illustrating how decision trees create split points from features like elevation and price, grow recursively, achieve high training accuracy, and reveal overfitting when evaluated on unseen test data.

Data visualizationartificial intelligenceclassification
0 likes · 11 min read
How Decision Trees Predict House Locations: From Intuition to Overfitting
Model Perspective
Model Perspective
Jun 19, 2022 · Artificial Intelligence

How Decision Trees Work: From Entropy to Gini Index Explained

This article introduces decision tree algorithms, explains their role in supervised learning for classification and regression, details the construction process, compares information gain and Gini index for attribute selection, and reviews popular tree methods such as ID3, C4.5, and CART with illustrative examples.

C4.5CARTGini Index
0 likes · 7 min read
How Decision Trees Work: From Entropy to Gini Index Explained
Model Perspective
Model Perspective
Jun 13, 2022 · Artificial Intelligence

Understanding Decision Trees: From Basic Process to Watermelon Example

This article explains the fundamentals of decision tree learning, describing its recursive construction, the criteria for splitting nodes using information gain based on entropy, and walks through a classic watermelon dataset example to illustrate how attributes are selected and the final tree is built.

ID3 algorithmInformation Gainclassification
0 likes · 8 min read
Understanding Decision Trees: From Basic Process to Watermelon Example
Baobao Algorithm Notes
Baobao Algorithm Notes
Jan 5, 2022 · Artificial Intelligence

When to Use Logistic Regression, SVM, Decision Trees, and More? A Practical Frequency Guide

This article analyzes how often common machine‑learning algorithms such as k‑NN, Naïve Bayes, decision trees, SVM, logistic regression, and neural networks are used in industry, explains their typical scenarios, highlights strengths and weaknesses, and shows how non‑linearity and feature engineering affect their suitability.

algorithm comparisondecision treefeature engineering
0 likes · 12 min read
When to Use Logistic Regression, SVM, Decision Trees, and More? A Practical Frequency Guide
Tencent Cloud Developer
Tencent Cloud Developer
Nov 19, 2021 · Artificial Intelligence

End‑to‑End Breast Cancer Prediction Solution Using Decision Tree on Tencent Cloud AI Platform

This guide details an end‑to‑end breast‑cancer prediction pipeline on Tencent Cloud, covering offline decision‑tree training with TI‑ONE, model packaging as a PMML service, real‑time feature generation via Oceanus and CKafka, and live inference stored in ClickHouse, all within a secure VPC.

AIFlinkReal-time Streaming
0 likes · 19 min read
End‑to‑End Breast Cancer Prediction Solution Using Decision Tree on Tencent Cloud AI Platform
Python Programming Learning Circle
Python Programming Learning Circle
Aug 10, 2021 · Artificial Intelligence

Building a Decision Tree Model in Python Using Entropy and Gini Impurity

This tutorial walks through creating, visualizing, and exporting two Python decision‑tree classifiers—one using entropy and the other using Gini impurity—by installing required packages, preparing a simple dataset, training the models with scikit‑learn, and rendering the trees with Graphviz.

Gini ImpurityGraphvizdecision tree
0 likes · 11 min read
Building a Decision Tree Model in Python Using Entropy and Gini Impurity
JD.com Experience Design Center
JD.com Experience Design Center
Apr 7, 2021 · Artificial Intelligence

How to Use CHAID Decision Trees in SPSS for Market Segmentation

This article explains why simple single‑feature analysis can miss important user groups, introduces decision trees—especially the CHAID algorithm—as a way to uncover multi‑attribute segments, and provides step‑by‑step instructions for building descriptive and predictive trees in SPSS, including how to interpret tree visuals and benefit tables.

CHAIDSPSSdata analysis
0 likes · 11 min read
How to Use CHAID Decision Trees in SPSS for Market Segmentation
NetEase Smart Enterprise Tech+
NetEase Smart Enterprise Tech+
Mar 9, 2021 · Information Security

How Server‑Side Device Fingerprinting Boosts Security and Stability

Device fingerprinting uniquely identifies devices using collected data; this article explains how uniqueness and stability are measured, shows probability‑based calculations for single and combined fields, discusses the shortcomings of client‑side methods, and details a server‑side multi‑algorithm approach that improves security and stability.

Securityanti-frauddecision tree
0 likes · 11 min read
How Server‑Side Device Fingerprinting Boosts Security and Stability
21CTO
21CTO
Sep 18, 2020 · Artificial Intelligence

Top 10 Essential Machine Learning Algorithms Every Data Scientist Should Know

This article provides a concise overview of ten fundamental machine learning algorithms—linear regression, logistic regression, linear discriminant analysis, naive Bayes, K‑nearest neighbors, learning vector quantization, decision trees, random forest, support vector machines, and boosting (AdaBoost)—explaining their core concepts, typical use‑cases, and practical considerations.

Naive BayesRandom ForestSupport Vector Machine
0 likes · 13 min read
Top 10 Essential Machine Learning Algorithms Every Data Scientist Should Know
Xianyu Technology
Xianyu Technology
Jul 28, 2020 · Operations

ShenTan: Automated Fault Localization System for Online Services

ShenTan is an automated fault‑localization platform for online services that quickly (under five seconds) pinpoints server‑side issues with developer‑level accuracy by aggregating real‑time metrics, applying a decision‑tree model enriched by expert knowledge and dynamic thresholds, and presenting results through an integrated alert and visualization system, while planning broader endpoint coverage and multi‑tenant support.

AutomationBig DataFault Localization
0 likes · 12 min read
ShenTan: Automated Fault Localization System for Online Services
Tencent Advertising Technology
Tencent Advertising Technology
May 2, 2020 · Artificial Intelligence

How to Use TI-ONE Built‑in Operators for the 2020 Tencent Advertising Algorithm Competition

This tutorial walks you through creating a TI‑ONE project, ingesting competition data, configuring and training a decision‑tree model with built‑in operators, running the workflow, and downloading and uploading the result files for the 2020 Tencent Advertising Algorithm Competition.

Model TrainingTI-ONEdata pipeline
0 likes · 7 min read
How to Use TI-ONE Built‑in Operators for the 2020 Tencent Advertising Algorithm Competition
21CTO
21CTO
Apr 12, 2019 · Artificial Intelligence

Top 10 Essential Machine Learning Algorithms Every Data Scientist Should Know

This article provides a concise overview of ten fundamental machine learning algorithms—linear regression, logistic regression, linear discriminant analysis, naive Bayes, K‑nearest neighbors, learning vector quantization, support vector machines, decision trees, bagging/random forest, and boosting/AdaBoost—explaining their principles, typical use cases, and key characteristics.

Naive BayesRandom ForestSupport Vector Machine
0 likes · 13 min read
Top 10 Essential Machine Learning Algorithms Every Data Scientist Should Know
Tencent Cloud Developer
Tencent Cloud Developer
Dec 4, 2018 · Artificial Intelligence

Top 10 Most Popular AI Algorithms

The article reviews the ten most popular AI algorithms—linear and logistic regression, LDA, decision trees, Naive Bayes, K‑Nearest Neighbors, LVQ, SVM, Random Forest, and deep neural networks—explaining their strengths, typical use cases, and why selecting the right model matters given the ‘no free lunch’ principle.

AI Algorithmsdecision treedeep neural network
0 likes · 12 min read
Top 10 Most Popular AI Algorithms
Qunar Tech Salon
Qunar Tech Salon
Oct 15, 2018 · Artificial Intelligence

Introduction to Decision Trees with scikit-learn

This article provides a comprehensive guide to decision tree algorithms, covering their theoretical background, classic use‑cases, scikit‑learn's DecisionTreeClassifier parameters, step‑by‑step Python examples for training, visualizing, and exporting trees, as well as a comparison of ID3, C4.5, and CART methods with their advantages and limitations.

Pythonclassificationdecision tree
0 likes · 20 min read
Introduction to Decision Trees with scikit-learn
AI Large-Model Wave and Transformation Guide
AI Large-Model Wave and Transformation Guide
Oct 8, 2018 · Artificial Intelligence

Build a CART Decision Tree from Scratch in Python – Full Step‑by‑Step Guide

This article walks through a complete Python implementation of the CART decision‑tree algorithm on the Banknote dataset, covering data loading, cross‑validation splitting, Gini impurity calculation, recursive tree construction, prediction, and performance evaluation with concrete code examples.

Banknote DatasetCARTGini Index
0 likes · 7 min read
Build a CART Decision Tree from Scratch in Python – Full Step‑by‑Step Guide
Efficient Ops
Efficient Ops
Jan 7, 2018 · Operations

How Tencent Leverages AI to Simplify Massive-Scale Service Monitoring and Root‑Cause Analysis

Tencent's SNG social platform team tackles billion‑scale traffic by integrating AI‑driven anomaly detection, multi‑dimensional monitoring, and decision‑tree based root‑cause analysis, turning complex backend architectures and massive alert volumes into streamlined, actionable insights for faster issue resolution.

AIOperationsanomaly detection
0 likes · 16 min read
How Tencent Leverages AI to Simplify Massive-Scale Service Monitoring and Root‑Cause Analysis
Architects' Tech Alliance
Architects' Tech Alliance
Nov 14, 2017 · Artificial Intelligence

Explaining Machine Learning to a Child: A Food‑Classification Example

The article uses a simple food‑taste classification scenario to illustrate core machine‑learning concepts such as labeled training data, feature representation, linear scoring models, decision boundaries, over‑fitting, generalisation, and decision‑tree reasoning in a way a child can understand.

AI basicsclassificationdecision tree
0 likes · 4 min read
Explaining Machine Learning to a Child: A Food‑Classification Example
MaGe Linux Operations
MaGe Linux Operations
Apr 5, 2017 · Artificial Intelligence

Master Decision Trees with the Iris Dataset: A Hands‑On Guide

This article introduces classification and decision‑tree algorithms, explains the Iris dataset, and provides step‑by‑step Python code using scikit‑learn to build, train, evaluate, and visualize decision‑tree models, including optimizations and practical tips for accurate predictions.

classificationdecision treeiris dataset
0 likes · 10 min read
Master Decision Trees with the Iris Dataset: A Hands‑On Guide
Baidu Intelligent Testing
Baidu Intelligent Testing
Jul 13, 2016 · Artificial Intelligence

Detecting Offline Merchant Service Issues Using Machine Learning and Big Data at Nuomi

The article describes how Nuomi analyzes refund and complaint data with machine‑learning and big‑data techniques, extracts features for single‑ and multi‑store scenarios, builds decision‑tree models with regional adjustments, and creates an online workflow to promptly intervene on merchants that fail to serve customers.

Big Datacustomer experiencedecision tree
0 likes · 5 min read
Detecting Offline Merchant Service Issues Using Machine Learning and Big Data at Nuomi
Efficient Ops
Efficient Ops
Jun 14, 2016 · Operations

Automate Fault Root‑Cause Detection in Massive IT Operations

This article explains how large‑scale internet companies can reduce alarm storms and speed up incident resolution by creating an operations ecosystem centered on automated fault root‑cause localization, detailing the challenges, architecture, decision‑tree algorithms, and a four‑step implementation guide.

AutomationIT infrastructureOperations
0 likes · 11 min read
Automate Fault Root‑Cause Detection in Massive IT Operations
Qunar Tech Salon
Qunar Tech Salon
Mar 15, 2015 · Artificial Intelligence

Overview of Common Classification Algorithms in Data Mining

This article introduces the concepts of classification and prediction in data mining, outlines their workflow, and provides concise explanations of six widely used classification techniques—decision trees, K‑Nearest Neighbour, Support Vector Machine, Vector Space Model, Bayesian methods, and neural networks—highlighting their principles, advantages, and limitations.

Bayesiandata miningdecision tree
0 likes · 9 min read
Overview of Common Classification Algorithms in Data Mining