hyperparameter tuning

Python Programming Learning Circle

Feb 8, 2025 · Artificial Intelligence

Random Forest Classification with PCA and Hyper‑Parameter Tuning on the Breast Cancer Dataset

This tutorial walks through loading the scikit‑learn breast‑cancer dataset, preprocessing it, building baseline and PCA‑reduced Random Forest models, applying RandomizedSearchCV and GridSearchCV for hyper‑parameter optimization, and evaluating the final models using recall as the primary metric.

Breast CancerPCAhyperparameter tuning

0 likes · 17 min read

Random Forest Classification with PCA and Hyper‑Parameter Tuning on the Breast Cancer Dataset

Python Programming Learning Circle

Jun 21, 2024 · Artificial Intelligence

Using scikit-learn for Data Mining: Feature Engineering, Parallel Processing, Pipelines, and Model Persistence

This article demonstrates how to perform data mining with scikit-learn by detailing the full workflow—from data acquisition and feature engineering, through parallel and pipeline processing, to automated hyper‑parameter tuning and model persistence—using the Iris dataset as an example.

Data MiningParallel ProcessingPipeline

0 likes · 13 min read

Using scikit-learn for Data Mining: Feature Engineering, Parallel Processing, Pipelines, and Model Persistence

Test Development Learning Exchange

Apr 4, 2024 · Artificial Intelligence

Scikit‑Optimize (skopt): Features, Use Cases, and Code Examples

Scikit‑Optimize is a Python library for black‑box optimization that offers adaptable, efficient algorithms, hyper‑parameter tuning, interactive monitoring, and seamless Scikit‑Learn integration, illustrated with five comprehensive code examples covering basic usage, constrained and interactive optimization, and visualization.

Bayesian Optimizationblack-box optimizationhyperparameter tuning

0 likes · 7 min read

Scikit‑Optimize (skopt): Features, Use Cases, and Code Examples

DataFunSummit

Nov 21, 2023 · Artificial Intelligence

Automatic Hyperparameter Tuning in Tencent Recommendation System (TRS): Techniques, Evolution, and Practice

This article presents an in‑depth overview of Tencent's TRS automatic hyperparameter tuning, covering background, challenges, the evolution from Bayesian optimization to evolution strategies and reinforcement learning, a systematic platform solution, real‑world deployment results, and a Q&A session.

Bayesian Optimizationevolution strategieshyperparameter tuning

0 likes · 20 min read

Automatic Hyperparameter Tuning in Tencent Recommendation System (TRS): Techniques, Evolution, and Practice

DataFunSummit

Feb 14, 2023 · Artificial Intelligence

Deep Learning Hyperparameter Tuning and Training Tips: Insights from Zhihu Experts

This article compiles practical deep learning training and hyperparameter tuning advice from Zhihu contributors, covering model debugging, learning‑rate strategies, optimizer choices, data preprocessing, regularization techniques, initialization methods, common pitfalls, recommended research papers, and ensemble approaches.

deep learninggradient clippinghyperparameter tuning

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Deep Learning Hyperparameter Tuning and Training Tips: Insights from Zhihu Experts

Python Programming Learning Circle

Apr 19, 2022 · Artificial Intelligence

Step‑by‑Step Guide to Building Machine Learning Models with Scikit‑learn Templates

This article introduces a practical, step‑by‑step tutorial on building machine learning models with scikit‑learn, covering problem types, dataset loading, splitting, and a series of reusable templates (V1.0, V2.0, V3.0) for classification, regression, clustering, cross‑validation, and hyper‑parameter tuning, complete with code examples.

classificationcross-validationhyperparameter tuning

0 likes · 17 min read

Step‑by‑Step Guide to Building Machine Learning Models with Scikit‑learn Templates

DataFunTalk

Jan 3, 2021 · Artificial Intelligence

iQIYI Machine Learning Platform: Development History, Features, and Practical Experience

This article details the evolution of iQIYI's machine learning platform—from its early Javis‑based deep‑learning system to three major versions that introduced visual workflow, distributed scheduling, auto‑tuning, large‑scale training support, model management, and online prediction—while sharing practical lessons and a real anti‑cheat use case.

Model Managementbig datahyperparameter tuning

0 likes · 13 min read

iQIYI Machine Learning Platform: Development History, Features, and Practical Experience