feature selection

Qunar Tech Salon

Feb 17, 2025 · Artificial Intelligence

Evolution of Qunar Hotel Search Ranking: From LambdaMart to LambdaDNN and Multi‑Objective Optimization

The article details Qunar’s hotel search ranking system evolution, covering the shift from rule‑based sorting to LambdaMart, the adoption of LambdaDNN deep models, multi‑objective MMOE architectures, multi‑scenario integration, extensive feature engineering, and experimental results demonstrating significant offline and online performance gains.

Deep LearningRecommendation systemsfeature selection

0 likes · 36 min read

Evolution of Qunar Hotel Search Ranking: From LambdaMart to LambdaDNN and Multi‑Objective Optimization

Test Development Learning Exchange

Nov 22, 2024 · Artificial Intelligence

Feature Selection and Feature Engineering with Python (Filter, Wrapper, and Embedded Methods)

This tutorial teaches how to perform feature selection using filter, wrapper, and embedded methods and how to construct new features such as interaction, non‑linear, binned, and binary features with Python's pandas and scikit‑learn libraries.

Pythonfeature engineeringfeature selection

0 likes · 7 min read

Feature Selection and Feature Engineering with Python (Filter, Wrapper, and Embedded Methods)

Python Programming Learning Circle

Mar 23, 2024 · Artificial Intelligence

Eight Python Libraries to Accelerate Data‑Science Workflows

This article introduces eight Python libraries—including Optuna, ITMO_FS, shap‑hypetune, PyCaret, floWeaver, Gradio, Terality, and Torch‑Handle—that streamline data‑science tasks such as hyperparameter optimization, feature selection, model building, visualization, and deployment, helping users save coding time and improve productivity.

Hyperparameter OptimizationLibrariesPython

0 likes · 12 min read

Eight Python Libraries to Accelerate Data‑Science Workflows

Model Perspective

Aug 31, 2023 · Artificial Intelligence

Master Feature Selection: From Filters to PCA with Python

This article explains why selecting the right features is essential for machine learning, outlines the general workflow, compares filter, wrapper, and embedded methods, demonstrates statistical tests and Python code examples, and shows how PCA can synthesize features for dimensionality reduction.

PCAPythonchi-square

0 likes · 18 min read

Master Feature Selection: From Filters to PCA with Python

Architects' Tech Alliance

Jul 11, 2023 · Artificial Intelligence

Wear-Updated Integrated Feature Ranking (WEFR) for Robust SSD Failure Prediction

The article presents a large‑scale study of SSD failure prediction using SMART logs from multiple vendors, introduces the Wear‑Updated Integrated Feature Ranking (WEFR) method to automatically and robustly select predictive features, and demonstrates its effectiveness through extensive experiments on real‑world data.

SSDWEFRWear Level

0 likes · 10 min read

Wear-Updated Integrated Feature Ranking (WEFR) for Robust SSD Failure Prediction

Model Perspective

Mar 20, 2023 · Artificial Intelligence

Master Feature Selection with Recursive Elimination (RFE) in Python

Feature Recursive Elimination (RFE) is a powerful feature‑selection technique that iteratively trains a model, discards the weakest features, and repeats until a desired number of features remains, helping prevent overfitting and improve model performance, illustrated with a complete Python example using scikit‑learn.

Pythonfeature selectionmachine learning

0 likes · 6 min read

Master Feature Selection with Recursive Elimination (RFE) in Python

Model Perspective

Feb 8, 2023 · Artificial Intelligence

Mastering Feature Selection: From Filters to Embedded Methods in Python

This article explains why feature selection is crucial for machine learning, outlines the general workflow, compares filter, wrapper, embedded, and synthesis approaches, and provides practical Python examples—including Pearson correlation, chi‑square tests, mutual information, variance selection, recursive elimination, L1 regularization, and PCA—complete with code snippets and visualizations.

Pythondimensionality reductionfeature selection

0 likes · 20 min read

Mastering Feature Selection: From Filters to Embedded Methods in Python

Architects' Tech Alliance

Dec 30, 2022 · Artificial Intelligence

Wear‑Updated Integrated Feature Ranking (WEFR) for SSD Failure Prediction

This article presents a large‑scale study of SSD failure prediction using SMART logs from Alibaba data centers, introduces the Wear‑Updated Integrated Feature Ranking (WEFR) method for robust feature selection across different drive models and wear levels, and demonstrates its effectiveness through extensive experiments.

SSDWear Levelfailure prediction

0 likes · 11 min read

Wear‑Updated Integrated Feature Ranking (WEFR) for SSD Failure Prediction

Python Programming Learning Circle

Oct 25, 2022 · Artificial Intelligence

Genetic Algorithms: Theory, Steps, and Practical Implementation with TPOT for Data Science

This article introduces genetic algorithms, explains their biological inspiration, details each step of the algorithm, demonstrates solving the knapsack problem, and provides a complete Python implementation using the TPOT library for feature selection and regression on the Big Mart Sales dataset.

PythonTPOTdata science

0 likes · 19 min read

Genetic Algorithms: Theory, Steps, and Practical Implementation with TPOT for Data Science

Python Programming Learning Circle

Feb 23, 2022 · Artificial Intelligence

A Survey of Python Libraries for Hyperparameter Optimization, Feature Selection, Model Explainability, and Rapid Machine Learning Development

This article introduces several Python libraries—including Optuna, ITMO_FS, shap‑hypertune, PyCaret, floWeaver, Gradio, Terality, and torch‑handle—that simplify hyperparameter tuning, feature selection, model explainability, visualization, and low‑code ML workflows, providing code examples and key advantages for each tool.

Hyperparameter OptimizationModel ExplainabilityPython

0 likes · 10 min read

A Survey of Python Libraries for Hyperparameter Optimization, Feature Selection, Model Explainability, and Rapid Machine Learning Development

DataFunSummit

Sep 10, 2021 · Artificial Intelligence

Advances in Pre‑Ranking: The COLD System for Large‑Scale Advertising

This article reviews the evolution of coarse‑ranking in large‑scale ad systems, explains the two main technical routes—set selection and precise value estimation—introduces the Computing‑Power‑Cost‑Aware Online Lightweight Deep (COLD) pre‑ranking framework, and presents experimental results and future directions for deeper integration with fine‑ranking.

COLDDeep Learningadvertising

0 likes · 21 min read

Advances in Pre‑Ranking: The COLD System for Large‑Scale Advertising

DataFunTalk

Jun 14, 2021 · Artificial Intelligence

From Massive to Compact: Model Compression Strategies for Large‑Scale CTR Prediction in Alibaba Search Advertising

This article describes how Alibaba's search advertising team transformed trillion‑parameter CTR models into lightweight, high‑precision systems by compressing embedding layers through feature‑space reduction, dimension quantization, and multi‑hash techniques, while also introducing graph‑based pre‑training and dropout‑driven feature selection to maintain accuracy.

CTR predictionGraph Neural Networksembedding reduction

0 likes · 15 min read

From Massive to Compact: Model Compression Strategies for Large‑Scale CTR Prediction in Alibaba Search Advertising

Alimama Tech

Jun 2, 2021 · Artificial Intelligence

Model Compression and Feature Optimization for Large-Scale CTR Prediction in Advertising

Alibaba‑Mama’s advertising team shrank multi‑terabyte CTR models to just tens of gigabytes by applying row‑dimension embedding compression, multi‑hash embeddings, graph‑based relationship networks, PCF‑GNN pre‑training, and droprank feature selection, preserving accuracy while halving training time, doubling online QPS, and retiring hundreds of servers.

CTR predictionGraph Neural NetworksLarge-scale ML

0 likes · 14 min read

Model Compression and Feature Optimization for Large-Scale CTR Prediction in Advertising

DataFunTalk

Jun 1, 2021 · Artificial Intelligence

Advances in Click‑Through Rate (CTR) Modeling: Optimizations Across Embedding, Hidden, and Output Layers

This article reviews recent academic and industrial advances in click‑through rate prediction, classifying optimization techniques for the three‑layer CTR architecture—Embedding, Hidden, and Output—while summarizing three SIGIR papers on graph‑based user behavior modeling, explicit semantic cross‑feature learning, and learnable feature selection for pre‑ranking.

Deep LearningGraph Neural NetworksRecommendation systems

0 likes · 11 min read

Advances in Click‑Through Rate (CTR) Modeling: Optimizations Across Embedding, Hidden, and Output Layers

Alimama Tech

May 27, 2021 · Artificial Intelligence

Towards a Better Tradeoff between Effectiveness and Efficiency in Pre‑Ranking: A Learnable Feature‑Selection‑Based Approach

The authors introduce an interaction‑focused pre‑ranking model combined with a learnable, complexity‑aware feature‑selection technique (FSCD) that selects a compact feature set, enabling Alibaba’s search advertising system to boost offline AUC from 0.695 to 0.737, raise recall to 95 %, improve CTR and RPM, yet retain CPU usage and latency comparable to traditional vector‑dot models.

Search Advertisingeffectivenessefficiency

0 likes · 15 min read

Towards a Better Tradeoff between Effectiveness and Efficiency in Pre‑Ranking: A Learnable Feature‑Selection‑Based Approach

Alimama Tech

May 27, 2021 · Artificial Intelligence

Advances in Click‑Through Rate (CTR) Modeling: Overview of Recent SIGIR Papers and Optimization Paths

The article reviews recent Alibaba Mama advances in click‑through‑rate modeling, classifying optimizations across the three‑layer CTR architecture and highlighting three SIGIR papers—GIN’s graph‑based user intent modeling, PCF’s pre‑trained GNN for explicit cross‑feature semantics, and FSCD’s compute‑factor‑guided automatic feature selection—each boosting prediction accuracy and system efficiency.

CTR predictionGraph Neural NetworksSearch Advertising

0 likes · 12 min read

Advances in Click‑Through Rate (CTR) Modeling: Overview of Recent SIGIR Papers and Optimization Paths

Python Programming Learning Circle

May 8, 2021 · Artificial Intelligence

Top 10 New Features in Scikit‑learn 0.24

The article reviews the most important additions in scikit‑learn 0.24, including faster hyper‑parameter search methods, ICE plots, histogram‑based boosting improvements, new feature‑selection tools, polynomial‑feature approximations, a semi‑supervised classifier, MAPE metric, enhanced OneHotEncoder and OrdinalEncoder handling, and a more flexible RFE interface.

Pythondata preprocessingfeature selection

0 likes · 8 min read

Top 10 New Features in Scikit‑learn 0.24

DataFunTalk

Mar 5, 2021 · Artificial Intelligence

Feature Selection Techniques for the Kaggle Mushroom Classification Dataset Using Python

This tutorial explains why and how to reduce the number of features in the Kaggle Mushroom Classification dataset with Python, covering preprocessing, various feature‑selection methods (filter, wrapper, embedded), code examples, model training, performance impact, and visualisation of results.

Mushroom datasetPythondata preprocessing

0 likes · 14 min read

Feature Selection Techniques for the Kaggle Mushroom Classification Dataset Using Python

TAL Education Technology

Sep 17, 2020 · Artificial Intelligence

Comprehensive Guide to Feature Engineering and Data Preprocessing for Machine Learning

This article provides an extensive overview of feature engineering, covering feature understanding, cleaning, construction, selection, transformation, and dimensionality reduction techniques, illustrated with Python code using the Titanic dataset, and offers practical guidelines for improving data quality and model performance in machine learning projects.

PythonTitanic datasetdata preprocessing

0 likes · 44 min read

Comprehensive Guide to Feature Engineering and Data Preprocessing for Machine Learning

DataFunTalk

Aug 18, 2020 · Artificial Intelligence

COLD: A Next‑Generation Pre‑Ranking System for Online Advertising

The article introduces COLD, a computing‑power‑aware online and lightweight deep pre‑ranking system for Alibaba's targeted ads, detailing its evolution from static CTR models to vector‑inner‑product models, its flexible network architecture with feature‑selection via SE blocks, engineering optimizations such as parallelism, column‑wise computation, Float16 and MPS, and demonstrates superior offline and online performance through extensive experiments.

COLDfeature selectionmachine learning

0 likes · 11 min read

COLD: A Next‑Generation Pre‑Ranking System for Online Advertising