Tagged articles

16 articles

Page 1 of 1

Oct 28, 2025 · Artificial Intelligence

8 Proven Ways to Boost Machine Learning Model Accuracy

This article outlines eight practical techniques—including data augmentation, handling missing values, feature engineering, algorithm selection, hyperparameter tuning, ensemble methods, and cross‑validation—to systematically improve the accuracy of Python machine‑learning models, supported by explanations, examples, and code snippets.

cross-validationdata preprocessingensemble methods

0 likes · 16 min read

8 Proven Ways to Boost Machine Learning Model Accuracy

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

Data STUDIO

Sep 5, 2025 · Artificial Intelligence

19 Elegant Sklearn Tricks for More Efficient Machine Learning

This article presents 19 practical Sklearn functions—ranging from outlier detection to hyper‑parameter search—that replace manual data‑science steps, each illustrated with concise code examples and performance comparisons.

Model EvaluationPipelinedata preprocessing

0 likes · 24 min read

19 Elegant Sklearn Tricks for More Efficient Machine Learning

Data Party THU

Aug 18, 2025 · Artificial Intelligence

Unlock XGBoost Performance: Master the Core Parameters

This article provides a detailed, visual guide to XGBoost's most important hyper‑parameters—such as max_depth, min_child_weight, learning_rate, gamma, subsample, colsample_bytree, scale_pos_weight, alpha, and lambda—explaining how each influences tree complexity, regularization, and model generalization, and offering practical examples for effective tuning.

Model OptimizationRegularizationXGBoost

0 likes · 12 min read

Unlock XGBoost Performance: Master the Core Parameters

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 CancerPCARandom Forest

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.

Pipelinedata miningfeature engineering

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 StrategiesOnline Learning

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 LearningRegularizationgradient clipping

0 likes · 13 min read

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.

Pythonclassificationcross-validation

0 likes · 17 min read

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

Code DAO

Dec 18, 2021 · Artificial Intelligence

Accelerating Gradient Boosting with CatBoost

This article explains how CatBoost implements gradient boosting, handles categorical features without preprocessing, lists its key advantages, details common training parameters, and provides a step‑by‑step regression example with code for fitting, cross‑validation, grid search, tree visualization, and parameter inspection.

CatBoostgradient boostinghyperparameter tuning

0 likes · 7 min read

Accelerating Gradient Boosting with CatBoost

Code DAO

Dec 11, 2021 · Artificial Intelligence

How to Optimize Machine Learning Hyperparameters with GridSearchCV

This article explains how GridSearchCV automates hyperparameter tuning for machine‑learning models, demonstrates its use with a RandomForest classifier on the breast‑cancer dataset—including code, cross‑validation, best‑parameter results, and discusses its advantages and scalability limits.

GridSearchCVRandomForestcross-validation

0 likes · 6 min read

How to Optimize Machine Learning Hyperparameters with GridSearchCV

Code DAO

Dec 8, 2021 · Artificial Intelligence

Optimizers and Schedulers in Neural Network Architecture: A Detailed Guide

This article explains how optimizers and learning‑rate schedulers work, how to configure their hyperparameters and parameter groups, and how to apply differential learning rates and adaptive schedules in PyTorch and Keras to improve model training and transfer‑learning performance.

KerasPyTorchhyperparameter tuning

0 likes · 10 min read

Optimizers and Schedulers in Neural Network Architecture: A Detailed Guide

MaGe Linux Operations

Feb 19, 2021 · Artificial Intelligence

Master the Perceptron: From Theory to Scikit‑Learn Implementation

This tutorial explains the Perceptron linear binary classifier, shows how to implement and evaluate it with Scikit‑Learn in Python, and demonstrates hyper‑parameter tuning for optimal accuracy on synthetic data.

Perceptronbinary classificationhyperparameter tuning

0 likes · 12 min read

Master the Perceptron: From Theory to Scikit‑Learn Implementation

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.

Big DataModel Managementhyperparameter tuning

0 likes · 13 min read

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

Hulu Beijing

Mar 28, 2019 · Artificial Intelligence

Mastering Bayesian Hyperparameter Optimization: A Practical Guide

This article explains what hyper‑parameters are, why their tuning is a black‑box problem, and how Bayesian optimization—using surrogate models, acquisition functions, and posterior inference—offers a more efficient alternative to grid or random search, while also listing popular open‑source tools and discussing its limitations.

Acquisition FunctionAutoMLBayesian Optimization

0 likes · 8 min read

Mastering Bayesian Hyperparameter Optimization: A Practical Guide