Tagged articles

Feature Importance

8 articles · Page 1 of 1

Jun 7, 2026 · Artificial Intelligence

Why Random Forest Beats Linear Regression: Robust Fitting and Clear Feature Importance

This article explains decision‑tree regression, its limitations, and how Random Forest regression—through bagging, random sub‑features, and averaging—reduces variance, provides out‑of‑bag error estimates, and offers interpretable feature importance, illustrated with a full Python example and visual analysis.

BaggingFeature ImportancePython

0 likes · 16 min read

Why Random Forest Beats Linear Regression: Robust Fitting and Clear Feature Importance

Model Perspective

Aug 5, 2023 · Artificial Intelligence

Can a Random Forest Predict Smoking Habits? 79% Accuracy Explained

This article analyzes a biomedical dataset to identify key factors influencing smoking status, performs descriptive and exploratory data analysis, selects important features with a Random Forest, builds a predictive model achieving about 79% accuracy, and discusses evaluation metrics and future improvements.

Feature ImportanceHealth DataRandom Forest

0 likes · 15 min read

Can a Random Forest Predict Smoking Habits? 79% Accuracy Explained

Model Perspective

Oct 30, 2022 · Artificial Intelligence

How ALE Plots Overcome Partial Dependence Limitations in ML

The Accumulated Local Effect (ALE) plot, introduced by Daniel W. Apley in 2016, addresses the correlation issue inherent in Partial Dependence Plots, offering unbiased, faster, and more accurate feature impact visualizations for machine‑learning models, especially in domains like financial risk control.

ALEFeature Importancemachine learning

0 likes · 9 min read

How ALE Plots Overcome Partial Dependence Limitations in ML

DataFunTalk

Oct 12, 2022 · Artificial Intelligence

Feature Embedding Modeling for Recommendation Systems: Techniques, Models, and Practical Insights from Weibo

This article presents a comprehensive overview of feature embedding modeling in recommendation systems, discussing the necessity of feature modeling, three technical directions (gate threshold, variable‑length embeddings, and enrichment), detailed descriptions of models such as FiBiNet, FiBiNet++, ContextNet, and MaskNet, experimental findings, and a Q&A session that addresses practical challenges and future work.

CTR modelsFeature ImportanceRecommendation Systems

0 likes · 34 min read

Feature Embedding Modeling for Recommendation Systems: Techniques, Models, and Practical Insights from Weibo

DataFunTalk

Jan 3, 2020 · Artificial Intelligence

Survey of Machine Learning Model Interpretability Techniques

This article provides a comprehensive survey of model interpretability in machine learning, covering its importance, evaluation criteria, and a wide range of techniques such as permutation importance, partial dependence plots, ICE, LIME, SHAP, RETAIN, and LRP, along with practical code examples and visualizations.

Feature ImportanceICELIME

0 likes · 39 min read

Survey of Machine Learning Model Interpretability Techniques

iQIYI Technical Product Team

Jun 28, 2019 · Artificial Intelligence

iQIYI's RSLIME: A Novel Feature Importance Analysis Method for Video Recommendation Systems

iQIYI introduces RSLIME, a model‑agnostic, sample‑level feature importance method for its three‑stage small‑video recommendation system, enabling interpretable analysis of a complex ranking module that combines DNN, GBDT, and FM, and demonstrating stable, AUC‑correlated insights for optimization and feature selection.

DNNFMFeature Importance

0 likes · 11 min read

iQIYI's RSLIME: A Novel Feature Importance Analysis Method for Video Recommendation Systems

AntTech

May 22, 2018 · Artificial Intelligence

Unpack Local Model Interpretation for GBDT – Summary and Analysis

This article summarizes the Ant Financial paper presented at DASFAA 2018 that proposes a universal local explanation method for Gradient Boosting Decision Tree models, detailing the problem definition, the PMML‑based algorithm for attributing feature contributions, experimental validation on fraud detection data, and the practical benefits for model transparency and improvement.

Feature ImportanceGBDTModel Interpretation

0 likes · 12 min read

Unpack Local Model Interpretation for GBDT – Summary and Analysis

Architects Research Society

Oct 28, 2016 · Artificial Intelligence

Phishing Website Detection Using Machine Learning Models in R

This article presents a step‑by‑step machine‑learning analysis of the UCI Phishing Websites dataset in R, loading the data, training boosted logistic regression, SVM, tree‑bagging, and random‑forest models, comparing their accuracies, and identifying the most important predictive features for phishing detection.

Feature ImportanceRRandom Forest

0 likes · 11 min read

Phishing Website Detection Using Machine Learning Models in R