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Model Perspective
Model Perspective
Aug 23, 2023 · Artificial Intelligence

Master Logistic Regression: Binary, Multiclass, and Ordered Extensions with Python

This article explains logistic regression and its extensions—binary, multiclass (softmax), and ordered logistic regression—covering mathematical foundations, optimization objectives, real‑world applications, and Python implementations using scikit‑learn with code examples and visual illustrations.

Pythonbinary classificationlogistic regression
0 likes · 15 min read
Master Logistic Regression: Binary, Multiclass, and Ordered Extensions with Python
Alimama Tech
Alimama Tech
May 13, 2021 · Artificial Intelligence

Fundamentals and Misconceptions of CTR (Click-Through Rate) Modeling

CTR modeling predicts click probabilities despite inherent microscopic randomness, treating each impression as an i.i.d. Bernoulli event and framing the task as binary classification; because data are noisy and imbalanced, evaluation relies on AUC rather than accuracy, with theoretical upper bounds set by feature quality, and calibration is needed to align predicted values with observed frequencies.

AUCCTRbinary classification
0 likes · 20 min read
Fundamentals and Misconceptions of CTR (Click-Through Rate) Modeling
JD Tech Talk
JD Tech Talk
Mar 29, 2019 · Artificial Intelligence

Understanding Confusion Matrix, ROC Curve, and Evaluation Metrics for Binary Classification Models

After building a binary classification model, this article explains essential evaluation tools such as the confusion matrix, derived metrics like accuracy, precision, recall, F1 score, and the ROC curve, illustrating their definitions, visualizations, and practical considerations for different business scenarios.

Evaluation MetricsF1 scoreROC curve
0 likes · 6 min read
Understanding Confusion Matrix, ROC Curve, and Evaluation Metrics for Binary Classification Models
UC Tech Team
UC Tech Team
Nov 5, 2018 · Artificial Intelligence

News Page Identification Using Machine Learning: Feature Engineering, Model Selection, and Evaluation

To accurately distinguish news pages from other web page types, this study formulates the task as a binary classification problem, extracts 19 engineered features from HTML, evaluates logistic regression and SVM models with cross‑validation, and achieves over 90% precision, recall, and F1‑score using LR with Newton method.

Web Crawlingbinary classificationfeature engineering
0 likes · 13 min read
News Page Identification Using Machine Learning: Feature Engineering, Model Selection, and Evaluation
Qunar Tech Salon
Qunar Tech Salon
Dec 5, 2017 · Information Security

Machine Learning Practices for Web Attack Detection at Ctrip

This article describes Ctrip’s evolution from rule‑based web attack detection to a Spark‑powered machine‑learning system, detailing the Nile architecture, data collection, feature engineering with TF‑IDF, model training, evaluation metrics, online deployment, and future enhancements for information security.

Web Securityattack detectionbinary classification
0 likes · 17 min read
Machine Learning Practices for Web Attack Detection at Ctrip
Hulu Beijing
Hulu Beijing
Nov 9, 2017 · Artificial Intelligence

Mastering ROC Curves: How to Plot and Compute AUC for Binary Classification

This article explains the fundamentals of ROC curve construction, the calculation of AUC, compares ROC with PR curves, and provides step‑by‑step examples—including a medical diagnosis scenario and threshold adjustments—to help readers accurately evaluate binary classification models.

AUCModel EvaluationROC
0 likes · 10 min read
Mastering ROC Curves: How to Plot and Compute AUC for Binary Classification