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Wu Shixiong's Large Model Academy
Wu Shixiong's Large Model Academy
Apr 22, 2026 · Artificial Intelligence

How to Classify and Manage Agent Memories for Better Retrieval

This article dissects Claude Code's memory system, explains why unstructured memory degrades performance, introduces four distinct memory types with concrete examples and schema, shows how to handle expiration and retrieval strategies, and provides step‑by‑step implementation code to improve agent reliability.

Agent MemoryLLMMemory Management
0 likes · 19 min read
How to Classify and Manage Agent Memories for Better Retrieval
Huolala Safety Emergency Response Center
Huolala Safety Emergency Response Center
Apr 15, 2026 · Information Security

How to Auto‑Label 10K APIs with 95% Confidence Using Self‑Learning Feature Engineering

This article presents a detailed case study of how a large‑scale API security team built an automated, self‑learning classification system that tags tens of thousands of APIs with business labels, improves model accuracy by five points, and maintains high precision through a confidence‑driven feedback loop.

API SecurityCatBoostSHAP
0 likes · 13 min read
How to Auto‑Label 10K APIs with 95% Confidence Using Self‑Learning Feature Engineering
Huolala Tech
Huolala Tech
Apr 15, 2026 · Information Security

How We Built a Self‑Learning API Classification System for Security

This article details a real‑world case study of how a large logistics platform created an automated, self‑evolving API asset‑classification pipeline—covering data collection, feature engineering, model training with CatBoost, confidence‑based label feedback, and lessons learned—to improve API security monitoring and reduce manual labeling effort.

API SecurityCatBoostSHAP
0 likes · 13 min read
How We Built a Self‑Learning API Classification System for Security
Design Hub
Design Hub
Mar 27, 2026 · Artificial Intelligence

What Problem Does Claude Code’s Auto Mode Actually Solve?

Anthropic’s new Auto Mode for Claude Code inserts a middle ground between manual approvals and unrestricted execution by letting the model approve low‑risk actions while blocking potentially dangerous ones, using a two‑stage classifier that evaluates intent and real‑world impact with concrete safety metrics.

AI SafetyAgent DesignClaude Code
0 likes · 12 min read
What Problem Does Claude Code’s Auto Mode Actually Solve?
JavaEdge
JavaEdge
Mar 26, 2026 · Information Security

How Claude Code’s Automatic Permission System Balances Security and Usability

The article analyzes Claude Code’s new automatic permission mode, detailing its three operation options, two‑layer classifier architecture, threat model, decision flow, rule customization, evaluation results, design trade‑offs, and future plans for improving AI‑driven security.

AI securityAutomated approvalClaude Code
0 likes · 10 min read
How Claude Code’s Automatic Permission System Balances Security and Usability
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
AI Code to Success
AI Code to Success
Mar 28, 2025 · Artificial Intelligence

Unlocking the Power of Support Vector Machines: Theory, Code, and Real‑World Uses

This comprehensive guide explores Support Vector Machines—from their historical roots and core mathematical principles to practical Python implementations, visualization techniques, and diverse applications such as image recognition, text classification, bioinformatics, and financial risk assessment—while also weighing their strengths and limitations.

PythonSupport Vector Machineclassification
0 likes · 19 min read
Unlocking the Power of Support Vector Machines: Theory, Code, and Real‑World Uses
AI Code to Success
AI Code to Success
Mar 13, 2025 · Artificial Intelligence

Unlocking K-Nearest Neighbors: Theory, Implementation, and Real-World Tips

This article provides a comprehensive guide to the K‑Nearest Neighbors algorithm, covering its intuitive principle, step‑by‑step workflow, distance metrics, strategies for selecting the optimal K via cross‑validation, Python implementation with scikit‑learn, advantages, limitations, and diverse application scenarios.

Pythonclassificationcross-validation
0 likes · 24 min read
Unlocking K-Nearest Neighbors: Theory, Implementation, and Real-World Tips
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
AI Code to Success
AI Code to Success
Feb 25, 2025 · Artificial Intelligence

Master Logistic Regression: Theory, Practice, and Real‑World Tips

This comprehensive guide explains logistic regression fundamentals, the role of the Sigmoid function, loss and optimization methods, step‑by‑step Python implementation with data preparation, model training, evaluation, hyper‑parameter tuning, handling over‑ and under‑fitting, multi‑class extensions, and diverse application scenarios across medicine, finance, e‑commerce, and text analysis.

Model EvaluationPythonclassification
0 likes · 23 min read
Master Logistic Regression: Theory, Practice, and Real‑World Tips
Python Programming Learning Circle
Python Programming Learning Circle
Jan 22, 2025 · Artificial Intelligence

A Visual Introduction to Machine Learning: Concepts, Categories, and Techniques

This article provides a clear, illustrated overview of machine learning, explaining its place within artificial intelligence, the main sub‑fields such as supervised and unsupervised learning, classic algorithms, ensemble methods, and practical examples to help beginners grasp core concepts.

Unsupervised Learningclassificationensemble methods
0 likes · 8 min read
A Visual Introduction to Machine Learning: Concepts, Categories, and Techniques
Model Perspective
Model Perspective
Sep 10, 2024 · Artificial Intelligence

Why Cross-Entropy Is the Key Loss Function for Classification Models

This article explains how loss functions evaluate model performance, contrasts regression’s mean squared error with classification’s cross‑entropy, describes one‑hot encoding and softmax outputs, and shows why higher predicted probabilities for the correct class yield lower loss, highlighting applications in image, language, and speech tasks.

Softmaxclassificationcross entropy
0 likes · 5 min read
Why Cross-Entropy Is the Key Loss Function for Classification Models
IT Services Circle
IT Services Circle
Jul 9, 2024 · Artificial Intelligence

Comparative Study of Classification Algorithms and Calibration Using Synthetic Data

This article presents a comprehensive case study that explains classification principles, shows the key formulas for logistic regression and SVM, and provides a full Python implementation that generates synthetic data, trains multiple classifiers, calibrates them, and visualizes calibration curves and probability histograms.

CalibrationPythonclassification
0 likes · 6 min read
Comparative Study of Classification Algorithms and Calibration Using Synthetic Data
Rare Earth Juejin Tech Community
Rare Earth Juejin Tech Community
Apr 7, 2024 · Artificial Intelligence

Logistic Regression: Definition, Purpose, Structure, Implementation, and Regularization

This article explains logistic regression as a classification algorithm, covering its definition, purpose, mathematical structure, data preparation, core functions such as sigmoid, cost, gradient descent, prediction, model evaluation, decision boundary visualization, feature mapping, and regularization techniques, all illustrated with Python code examples.

classificationlogistic regression
0 likes · 33 min read
Logistic Regression: Definition, Purpose, Structure, Implementation, and Regularization
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
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
Aug 26, 2023 · Artificial Intelligence

Why Accuracy Isn’t Enough: Mastering MCC for Imbalanced Classification

This article reviews common classification evaluation metrics—accuracy, precision, recall, and F1—explains their limitations on imbalanced data, and introduces the Matthews Correlation Coefficient (MCC) with Python implementations to provide a more reliable performance measure.

Evaluation MetricsMCCPython
0 likes · 5 min read
Why Accuracy Isn’t Enough: Mastering MCC for Imbalanced Classification
Rare Earth Juejin Tech Community
Rare Earth Juejin Tech Community
Jul 26, 2023 · Artificial Intelligence

Building and Training a Fully Connected Neural Network for Fashion-MNIST Classification with PyTorch

This tutorial demonstrates how to download the Fashion‑MNIST dataset, build a four‑layer fully connected neural network with PyTorch, and train it using loss functions, Adam optimizer, learning‑rate strategies, and Dropout to achieve high‑accuracy multi‑class image classification.

AdamDeep LearningDropout
0 likes · 17 min read
Building and Training a Fully Connected Neural Network for Fashion-MNIST Classification with PyTorch
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
Dec 7, 2022 · Artificial Intelligence

Predicting the 2022 FIFA World Cup Champion Using Machine Learning Models

This article details a data‑mining project that uses historical World Cup match data, extensive feature engineering, and various machine‑learning algorithms—including neural networks, logistic regression, SVM, decision trees, and random forests—to predict the champion of the 2022 tournament, while analyzing model errors and proposing improvements.

Model EvaluationWorld Cupclassification
0 likes · 7 min read
Predicting the 2022 FIFA World Cup Champion Using Machine Learning Models
Airbnb Technology Team
Airbnb Technology Team
Nov 3, 2022 · Artificial Intelligence

T-LEAF: A Taxonomy Learning and Evaluation Framework for Airbnb Community Support Classification System

The T‑LEAF framework introduces quantitative metrics for coverage, usefulness, and consistency to iteratively develop Airbnb’s unified Contact‑Reason taxonomy, enabling faster feedback loops, reducing “Other” classifications, and improving both human annotation agreement and machine‑learning prediction accuracy in production.

Evaluation Frameworkclassificationcommunity support
0 likes · 14 min read
T-LEAF: A Taxonomy Learning and Evaluation Framework for Airbnb Community Support Classification System
Model Perspective
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
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.

LightGBMPythonclassification
0 likes · 5 min read
Boost Your Models with LightGBM: Fast, Accurate Gradient Boosting in Python
Model Perspective
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.

PythonXGBoostboosting
0 likes · 4 min read
Master XGBoost: Boosting Trees Explained with Python Code
Model Perspective
Model Perspective
Aug 4, 2022 · Artificial Intelligence

How Support Vector Machines Classify Data: Core Principles Explained

Support Vector Machines (SVM), introduced in 1992, are powerful data‑mining methods based on statistical learning theory that excel at handling small‑sample, nonlinear, and high‑dimensional regression and classification tasks, with distinct formulations for classification (SVC) and regression (SVR).

classificationsvm
0 likes · 5 min read
How Support Vector Machines Classify Data: Core Principles Explained
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.

Artificial IntelligenceData visualizationclassification
0 likes · 11 min read
How Decision Trees Predict House Locations: From Intuition to Overfitting
Model Perspective
Model Perspective
Jun 22, 2022 · Artificial Intelligence

Understanding Model Performance: Precision, Recall, and F1 Score Explained

This article explains how to evaluate classification models by moving beyond simple accuracy to using confusion matrices, precision, recall, and the F1 score, illustrating their trade‑offs and when each metric is most appropriate for different real‑world scenarios.

F1 scoreclassificationconfusion matrix
0 likes · 4 min read
Understanding Model Performance: Precision, Recall, and F1 Score Explained
Model Perspective
Model Perspective
Jun 18, 2022 · Artificial Intelligence

Understanding Support Vector Machines: Theory, Example, and Python Code

This article explains the fundamentals of Support Vector Machines, describes how they separate data with optimal hyperplanes, provides a 2‑D example with visualizations, and includes Python code using scikit‑learn to generate synthetic data, plot points, and illustrate possible decision boundaries.

Support Vector Machineclassificationmachine learning
0 likes · 4 min read
Understanding Support Vector Machines: Theory, Example, and Python Code
Model Perspective
Model Perspective
Jun 17, 2022 · Artificial Intelligence

What Is Classification in Data Mining? Types, Models, and Key Applications

The article explains classification as a data‑analysis task that builds models to assign new observations to predefined categories, outlines its implementation steps, describes various data types (boolean, nominal, ordinal, continuous, discrete), presents common machine‑learning classifiers such as decision trees and neural networks, and highlights practical applications like crime detection, disease risk prediction, and credit assessment.

Model Evaluationclassificationdata mining
0 likes · 5 min read
What Is Classification in Data Mining? Types, Models, and Key Applications
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
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
Python Programming Learning Circle
Python Programming Learning Circle
Apr 19, 2022 · Artificial Intelligence

Step‑by‑Step Guide to Building Machine Learning Models with Scikit‑learn Templates

This article introduces a practical, step‑by‑step tutorial on building machine learning models with scikit‑learn, covering problem types, dataset loading, splitting, and a series of reusable templates (V1.0, V2.0, V3.0) for classification, regression, clustering, cross‑validation, and hyper‑parameter tuning, complete with code examples.

Pythonclassificationcross-validation
0 likes · 17 min read
Step‑by‑Step Guide to Building Machine Learning Models with Scikit‑learn Templates
Code DAO
Code DAO
Dec 3, 2021 · Artificial Intelligence

SMOTE Techniques for Handling Imbalanced Classification in Machine Learning

This article explains the SMOTE oversampling method for imbalanced classification, demonstrates how to generate synthetic minority samples, evaluates models with and without SMOTE using scikit‑learn pipelines, and explores advanced variants such as Borderline‑SMOTE, SVMSMOTE and ADASYN with concrete code examples and benchmark results.

SMOTEclassificationimbalanced learning
0 likes · 24 min read
SMOTE Techniques for Handling Imbalanced Classification in Machine Learning
IT Architects Alliance
IT Architects Alliance
Sep 25, 2021 · Big Data

Top 10 Classic Data Mining Algorithms and Their Core Characteristics

This article introduces the ten classic data‑mining algorithms selected by IEEE ICDM—C4.5, k‑Means, SVM, Apriori, EM, PageRank, AdaBoost, k‑NN, Naive Bayes, and CART—explaining their main ideas, advantages, and typical applications for readers seeking a solid foundation in data analysis.

Algorithmsclassificationclustering
0 likes · 8 min read
Top 10 Classic Data Mining Algorithms and Their Core Characteristics
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
iQIYI Technical Product Team
iQIYI Technical Product Team
May 21, 2021 · Big Data

Design and Implementation of iQIYI's User Feedback Analysis System

iQIYI built an in‑house user‑feedback analysis system that automatically ingests multi‑channel data, classifies and clusters issues, assesses feedback quality, localizes problems, and streamlines repair closure, boosting recall accuracy, alarm precision, closure rates and reducing cycle time across business lines to enhance user experience.

Big Dataaiclassification
0 likes · 15 min read
Design and Implementation of iQIYI's User Feedback Analysis System
Python Programming Learning Circle
Python Programming Learning Circle
Dec 18, 2020 · Artificial Intelligence

Understanding the Bayesian Formula and Naive Bayes Classifiers with Scikit-learn

This article explains the Bayesian theorem, introduces the Bayesian classifier, and details three Naive Bayes algorithms—Gaussian, Multinomial, and Bernoulli—along with their Scikit-learn implementations, key parameters, attributes, methods, and typical text‑classification applications for spam filtering.

Artificial IntelligenceBayesianNaive Bayes
0 likes · 8 min read
Understanding the Bayesian Formula and Naive Bayes Classifiers with Scikit-learn
Alibaba Cloud Developer
Alibaba Cloud Developer
Nov 24, 2020 · Fundamentals

Why Classification Thinking Is the Core of Application Architecture

This article explains how classification thinking—organizing software into modules, components, and packages—forms the foundation of application architecture, illustrates the COLA framework’s evolution, and shows how proper layering and domain‑oriented packaging improve maintainability and scalability.

COLAComponentPackage
0 likes · 15 min read
Why Classification Thinking Is the Core of Application Architecture
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
Python Crawling & Data Mining
Python Crawling & Data Mining
Aug 5, 2020 · Artificial Intelligence

Master Random Forest: From Bagging Theory to Python Implementation

This article explains the fundamentals of ensemble learning and bagging, details the random forest algorithm, answers common questions, and provides a complete Python walkthrough—including data exploration, decision‑tree baseline, random‑forest modeling with grid‑search tuning, and practical insights for handling imbalanced and missing data.

PythonRandom Forestbagging
0 likes · 16 min read
Master Random Forest: From Bagging Theory to Python Implementation
TAL Education Technology
TAL Education Technology
Jun 4, 2020 · Information Security

Data Security Governance: Motivation, Technical Objectives, Classification, and Management Practices

The article explains why data security governance is essential for rapidly growing businesses, outlines technical goals across the data lifecycle, describes data classification and labeling methods, and details approval processes, network security zones, and management controls to protect data throughout its lifecycle.

classificationdata securitygovernance
0 likes · 10 min read
Data Security Governance: Motivation, Technical Objectives, Classification, and Management Practices
Python Programming Learning Circle
Python Programming Learning Circle
May 7, 2020 · Artificial Intelligence

Understanding the k-Nearest Neighbor (kNN) Classification Algorithm and Its Python Implementation

This article introduces the concept and intuition behind the k-Nearest Neighbor (kNN) classification algorithm, explains its simple and full forms, discusses feature engineering and Euclidean distance calculations, and provides a complete Python implementation with example code.

classificationeuclidean distancefeature engineering
0 likes · 10 min read
Understanding the k-Nearest Neighbor (kNN) Classification Algorithm and Its Python Implementation
Alibaba Cloud Developer
Alibaba Cloud Developer
Mar 26, 2020 · Artificial Intelligence

How Amap Uses AI to Automate Millions of User Feedback Reports

This article describes how Gaode Map leverages machine‑learning techniques—such as word2vec embeddings, LSTM networks, fine‑tuning, and confidence‑threshold ensembles—to automatically classify and verify massive user‑feedback intelligence, streamlining the multi‑step workflow from data collection to road‑map updates and dramatically improving efficiency.

LSTMNLPai
0 likes · 16 min read
How Amap Uses AI to Automate Millions of User Feedback Reports
Python Programming Learning Circle
Python Programming Learning Circle
Mar 7, 2020 · Artificial Intelligence

k-Nearest Neighbors (kNN) Algorithm: Overview, Pros/Cons, Data Preparation, Implementation, and Handwritten Digit Recognition

This article explains the k‑Nearest Neighbors classification method, discusses its advantages and drawbacks, describes data preparation and normalization, presents Python code for the algorithm and a full handwritten digit recognition project, and reports an error rate of about 1.2%.

classificationeuclidean distancehandwritten digit recognition
0 likes · 9 min read
k-Nearest Neighbors (kNN) Algorithm: Overview, Pros/Cons, Data Preparation, Implementation, and Handwritten Digit Recognition
Alibaba Cloud Developer
Alibaba Cloud Developer
Oct 23, 2019 · Fundamentals

Master the Golden Three‑Step Method: From Daily Tasks to Complex Architecture

This article introduces Zhang Jianfei’s golden three‑step problem‑solving framework—define, decompose, and classify—illustrates its application in everyday chores, work reporting, code development, and system design, and connects it to taxonomy, the MECE principle, and common thinking models to boost structured thinking.

MECEMethodologyProduct Design
0 likes · 13 min read
Master the Golden Three‑Step Method: From Daily Tasks to Complex Architecture
DataFunTalk
DataFunTalk
Jul 5, 2019 · Artificial Intelligence

Lead Quality Prediction for Real Estate: Data, Modeling, and Interpretability

This article presents a case study on building and deploying a lead‑quality classification model for a high‑value, low‑frequency real‑estate platform, covering business context, data challenges, sampling strategies, feature engineering, model selection, tuning, evaluation metrics, interpretability analysis, and observed performance improvements.

Real EstateSamplingclassification
0 likes · 14 min read
Lead Quality Prediction for Real Estate: Data, Modeling, and Interpretability
Beike Product & Technology
Beike Product & Technology
Jun 28, 2019 · Artificial Intelligence

Building a Comprehensive Tagging System for Real‑Estate Recommendation at Beike

This article explains how Beike, China’s largest residential service platform, leverages its massive house, client, and text data to design a multi‑layered tag architecture, detailing data sources, tag construction methods—including classification, keyword, geographic, anonymous topic, and temporal tags—and their application to improve personalized house search and recommendation.

NLPReal EstateTagging
0 likes · 14 min read
Building a Comprehensive Tagging System for Real‑Estate Recommendation at Beike
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
Qunar Tech Salon
Qunar Tech Salon
Jan 16, 2019 · Artificial Intelligence

Introduction to Naive Bayes Classifier with scikit-learn

This article introduces the Naive Bayes classification algorithm, explains its theoretical basis, demonstrates how to use scikit-learn's GaussianNB class with Python code, evaluates model performance, and discusses advantages, limitations, and practical examples of the method.

Naive BayesPythonclassification
0 likes · 11 min read
Introduction to Naive Bayes Classifier with scikit-learn
Alibaba Cloud Infrastructure
Alibaba Cloud Infrastructure
Nov 9, 2018 · Artificial Intelligence

Predicting Server Memory Failures with Machine Learning: Feature Selection, Data Preprocessing, and Model Evaluation

This article presents a machine‑learning approach to predict DRAM failures in large‑scale data centers by analyzing server logs, selecting state, log, and static features through statistical tests and mutual information, preprocessing the data, and employing a tree‑based ensemble classifier that outperforms industry baselines.

Predictive Maintenanceclassificationfeature selection
0 likes · 7 min read
Predicting Server Memory Failures with Machine Learning: Feature Selection, Data Preprocessing, and Model Evaluation
Architects' Tech Alliance
Architects' Tech Alliance
Oct 24, 2018 · Operations

Data Center Facility Construction Standards and Classification Guidelines

This article outlines the scope, terminology, classification levels, site selection principles, equipment layout, and subsystem requirements—including lighting, grounding, lightning protection, HVAC, monitoring, and cabling—for building and operating data center facilities in accordance with industry standards.

Operationsclassificationconstruction standards
0 likes · 9 min read
Data Center Facility Construction Standards and Classification Guidelines
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
Tencent Cloud Developer
Tencent Cloud Developer
Oct 11, 2018 · Artificial Intelligence

Demystifying Neural Networks: A Mathematical Approach (Part 1)

The article mathematically demystifies neural networks by first illustrating a linear predictor for kilometre‑to‑mile conversion and a basic bug classifier, then exposing the limits of single linear boundaries (e.g., XOR), before introducing artificial neurons, activation functions, and multi‑layer weight‑adjustment training.

Artificial NeuronPredictionactivation functions
0 likes · 15 min read
Demystifying Neural Networks: A Mathematical Approach (Part 1)
Qunar Tech Salon
Qunar Tech Salon
Sep 19, 2018 · Artificial Intelligence

Logistic Regression Tutorial with scikit-learn

This article introduces logistic regression, explains its theoretical basis, details key scikit-learn parameters, and provides a complete Python example for breast cancer classification, covering data preprocessing, model training, prediction, and evaluation with classification reports.

Pythonclassificationdata preprocessing
0 likes · 7 min read
Logistic Regression Tutorial with scikit-learn
360 Quality & Efficiency
360 Quality & Efficiency
Jun 4, 2018 · Artificial Intelligence

Common Engineering Algorithms and Their Testing Methods

This article introduces the most commonly used algorithms in engineering—recommendation, optimization, estimation, and classification—describes their typical application scenarios, and explores various testing methods and evaluation metrics such as offline experiments, user surveys, A/B testing, and performance indicators like accuracy, coverage, and robustness.

Evaluation MetricsRecommendation Systemsalgorithm testing
0 likes · 12 min read
Common Engineering Algorithms and Their Testing Methods
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
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
Architects' Tech Alliance
Architects' Tech Alliance
Nov 24, 2016 · Big Data

Data Mining Overview: Process, Techniques, and Model Evaluation

This article provides a comprehensive introduction to data mining, covering its definition, goal setting, data sampling, exploration, preprocessing, pattern discovery, model building, evaluation methods, and the main analytical techniques such as classification, regression, clustering, association rules, feature and deviation analysis, and web mining.

Model Evaluationassociation rulesclassification
0 likes · 10 min read
Data Mining Overview: Process, Techniques, and Model Evaluation
StarRing Big Data Open Lab
StarRing Big Data Open Lab
Nov 4, 2016 · Artificial Intelligence

How Item Features Power Music Recommendations: A Hands‑On Guide

This article explains how recommendation systems can use item‑level features instead of user ratings, illustrating the approach with Pandora's music‑gene project, detailing feature selection, scoring, distance calculations, standardization, and classification techniques across music, athlete, Iris, and automobile datasets.

Recommendation Systemsclassificationdistance metrics
0 likes · 20 min read
How Item Features Power Music Recommendations: A Hands‑On Guide
Model Perspective
Model Perspective
Jun 2, 2016 · Fundamentals

Why Classifying Teaching Environments Is Essential for Modeling New Teachers

Classifying teaching environments helps simplify complex educational contexts, identify key variables, and build models—whether statistical, numerical, or mechanistic—to understand how factors like classroom dynamics, teacher‑student relationships, and colleague interactions influence a novice teacher’s development.

ModelingVariablesclassification
0 likes · 3 min read
Why Classifying Teaching Environments Is Essential for Modeling New Teachers
Qunar Tech Salon
Qunar Tech Salon
Feb 6, 2016 · Big Data

An Introduction to Data Mining Algorithms and Their Real-World Applications

This article introduces the main types of data‑mining algorithms—classification, prediction, clustering, and association—explains supervised and unsupervised learning, and illustrates each with practical examples such as spam detection, tumor cell identification, wine quality assessment, fraud detection, recommendation systems, and more.

association analysisclassificationclustering
0 likes · 15 min read
An Introduction to Data Mining Algorithms and Their Real-World Applications
Architect
Architect
Feb 1, 2016 · Big Data

An Introduction to Data Mining Algorithms and Their Real-World Applications

This article introduces the main types of data‑mining algorithms—classification, prediction, clustering, and association—explains supervised and unsupervised learning, and illustrates each with practical examples such as spam detection, tumor identification, wine quality assessment, fraud detection, recommendation systems, and authorship analysis.

anomaly detectionclassificationdata mining
0 likes · 14 min read
An Introduction to Data Mining Algorithms and Their Real-World Applications
21CTO
21CTO
Jan 6, 2016 · Artificial Intelligence

How to Build an End‑to‑End Marketplace Recommendation System: Product, Algorithms & Implementation

This article walks through designing and implementing a full‑stack recommendation system for 58转转, covering product frameworks, user and item profiling, RFM modeling, personalized tagging, classification‑based and collaborative‑filtering approaches, and practical deployment tips.

RFM modelclassificationcollaborative filtering
0 likes · 8 min read
How to Build an End‑to‑End Marketplace Recommendation System: Product, Algorithms & Implementation
Model Perspective
Model Perspective
Jun 23, 2015 · Fundamentals

How I Mapped the Interconnections Between Major Math Disciplines

After completing three years of university mathematics courses, the author visualises and explains a personal classification of major mathematical branches, discussing their relationships, cross‑disciplinary links, and reflections on ancient Chinese mathematics and future educational considerations.

Geometryclassificationfundamentals
0 likes · 5 min read
How I Mapped the Interconnections Between Major Math Disciplines
Qunar Tech Salon
Qunar Tech Salon
Mar 28, 2015 · Artificial Intelligence

Support Vector Machines in R: Theory, Implementation, and Parameter Tuning

This article explains how support vector machines work, how to handle non‑linear and multi‑class problems, and provides a complete R implementation using the e1071 package, including linear and radial kernels, model evaluation, parameter tuning, and visualisation with grid plots.

Grid PlotParameter TuningR
0 likes · 9 min read
Support Vector Machines in R: Theory, Implementation, and Parameter Tuning