ensemble methods

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.

artificial intelligenceclassificationensemble methods

0 likes · 8 min read

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

Qunar Tech Salon

Apr 26, 2018 · Artificial Intelligence

Understanding gcForest: Cascade Forest Structure and Multi‑grained Scanning for Representation Learning

The article explains how gcForest, an ensemble‑of‑decision‑tree model that mimics deep neural network hierarchies, uses cascade forests and multi‑grained sliding‑window scanning to achieve effective representation learning with fewer hyper‑parameters, especially on small datasets.

cascade forestensemble methodsgcForest

0 likes · 11 min read

Understanding gcForest: Cascade Forest Structure and Multi‑grained Scanning for Representation Learning

Qunar Tech Salon

Apr 3, 2018 · Artificial Intelligence

An Introduction to Gradient Boosting Decision Trees (GBDT) and Its Applications in Consumer Finance

Gradient Boosting Decision Tree (GBDT) is an ensemble learning method that combines additive and gradient boosting, detailed with its mathematical foundations, regression and classification algorithms, implementation using scikit‑learn, and a real‑world consumer‑finance fraud detection case achieving high AUC and KS metrics.

Consumer FinanceGBDTGradient Boosting

0 likes · 11 min read

An Introduction to Gradient Boosting Decision Trees (GBDT) and Its Applications in Consumer Finance

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.

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0 likes · 12 min read

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