BestHub
Sign in
Tagged articles
8 articles
Page 1 of 1
Data Party THU
Data Party THU
Apr 9, 2026 · Fundamentals

Mastering Numeric Feature Scaling: 4 Techniques with Scikit‑Learn

This article explains why numeric feature engineering is essential for machine learning, outlines the challenges of differing scales and outliers, and demonstrates four preprocessing methods—Standardization, Robust Scaler, Power Transformer, and Normalization—using the California housing dataset with detailed code examples and visual analysis.

feature scalingnormalizationnumeric preprocessing
0 likes · 11 min read
Mastering Numeric Feature Scaling: 4 Techniques with Scikit‑Learn
DeepHub IMBA
DeepHub IMBA
Mar 22, 2026 · Artificial Intelligence

Four Numeric Scaling Techniques: When to Use Standard, Robust, Power, and Min‑Max

This article explains why numeric feature engineering is essential for machine‑learning models, outlines the two main challenges of differing magnitudes and outliers, and demonstrates four scaling methods—StandardScaler, RobustScaler, PowerTransformer, and MinMaxScaler—using the California housing dataset, complete with code, visualizations, and guidance on when each method is appropriate.

feature scalingmin-max scalingpower transformer
0 likes · 13 min read
Four Numeric Scaling Techniques: When to Use Standard, Robust, Power, and Min‑Max
Data Party THU
Data Party THU
Feb 2, 2026 · Fundamentals

Why Standardize Data to Mean 0 and Variance 1?

The article explains that setting the mean to zero recenters data around the origin, making optimization algorithms converge faster, while scaling variance to one equalizes feature scales so no single feature dominates, illustrated with examples and visualizations of how standardization improves machine‑learning models.

data preprocessingfeature scalingmachine learning
0 likes · 5 min read
Why Standardize Data to Mean 0 and Variance 1?
Data STUDIO
Data STUDIO
Sep 18, 2025 · Artificial Intelligence

40 Essential Machine Learning Interview Questions and Answers for Fall 2025

This article presents a comprehensive set of 40 machine‑learning interview questions covering fundamental concepts such as the F1 score, logistic regression, activation functions, bias‑variance trade‑off, ensemble methods, feature scaling, cross‑validation, PCA, and hyper‑parameter optimization, each followed by concise, explanatory answers.

Bias-Variance TradeoffF1 scorecross-validation
0 likes · 34 min read
40 Essential Machine Learning Interview Questions and Answers for Fall 2025
Alimama Tech
Alimama Tech
Jul 5, 2023 · Artificial Intelligence

Maria: Multi-Scenario Ranking with Adaptive Feature Learning

Maria is a multi‑scenario ranking framework that adaptively learns features across heterogeneous e‑commerce query types—visual search, similar‑product search, and interest search—by employing Feature Scaling, Feature Refinement, and Feature Correlation Modeling modules, achieving superior performance and reducing the seesaw effect on the Ali‑CCP and Alimama datasets.

CTR predictionE-commerce Searchadaptive feature learning
0 likes · 11 min read
Maria: Multi-Scenario Ranking with Adaptive Feature Learning
Python Programming Learning Circle
Python Programming Learning Circle
Dec 31, 2022 · Artificial Intelligence

A Beginner’s Guide to Data Preprocessing for Machine Learning in Python

This tutorial walks beginners through the essential steps of data preprocessing for any machine learning model, covering library imports, dataset loading, handling missing values, encoding categorical features, splitting into train‑test sets, and applying feature scaling using Python’s scikit‑learn.

Pythondata preprocessingfeature scaling
0 likes · 11 min read
A Beginner’s Guide to Data Preprocessing for Machine Learning in Python
Alibaba Cloud Developer
Alibaba Cloud Developer
Dec 28, 2018 · Artificial Intelligence

Elastic Feature Scaling: Boosting Alibaba’s Online Recommendation CTR by 4%

This article describes how Ant Financial’s AI team redesigned TensorFlow to enable elastic feature scaling, introduced a Group‑Lasso optimizer and streaming frequency filtering, compressed models by 90%, and achieved significant CTR and efficiency gains in Alipay’s online recommendation system.

Online LearningRecommendation SystemsTensorFlow
0 likes · 20 min read
Elastic Feature Scaling: Boosting Alibaba’s Online Recommendation CTR by 4%