random forest | BestHub

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

Tencent Cloud Developer

Jul 4, 2024 · Artificial Intelligence

Football Match Outcome Prediction and Betting Strategy Using Machine Learning

The study combines team statistics and bookmaker odds with machine‑learning models—including Poisson, regression, Bayesian, SVM, Random Forest, DNN, and LSTM—to predict football match outcomes, identify confidence‑based betting intervals that yield profit, and suggests extensions to broader data, features, and financial trading.

Data MiningSVMfootball prediction

0 likes · 23 min read

Football Match Outcome Prediction and Betting Strategy Using Machine Learning

Python Programming Learning Circle

Apr 10, 2024 · Artificial Intelligence

Top 10 Machine Learning Algorithms Explained

This article introduces the No‑Free‑Lunch principle in machine learning and provides concise explanations of ten fundamental algorithms—including linear and logistic regression, LDA, decision trees, Naïve Bayes, K‑Nearest Neighbors, LVQ, SVM, bagging with random forests, and boosting with AdaBoost—guiding beginners on how to choose the right model.

AIAlgorithmsSVM

0 likes · 14 min read

Top 10 Machine Learning Algorithms Explained

Model Perspective

Aug 8, 2023 · Artificial Intelligence

Predicting Tomorrow’s Weather with Random Forests: A European City Case Study

Using detailed meteorological records from 18 European cities between 2000 and 2010, this article demonstrates how random forest regression and comprehensive data preprocessing can forecast daily precipitation, evaluate model performance, and compare climatic patterns across cities, highlighting both strengths and limitations of the approach.

climate datamachine learningrandom forest

0 likes · 20 min read

Predicting Tomorrow’s Weather with Random Forests: A European City Case Study

Model Perspective

Aug 5, 2023 · Artificial Intelligence

Can a Random Forest Predict Smoking Habits? 79% Accuracy Explained

This article analyzes a biomedical dataset to identify key factors influencing smoking status, performs descriptive and exploratory data analysis, selects important features with a Random Forest, builds a predictive model achieving about 79% accuracy, and discusses evaluation metrics and future improvements.

feature importancehealth datamachine learning

0 likes · 15 min read

Can a Random Forest Predict Smoking Habits? 79% Accuracy Explained

Model Perspective

Jan 20, 2023 · Artificial Intelligence

Visualizing Random Forest Decision Boundaries on the Wine Dataset with dtreeviz

This tutorial demonstrates how to load the wine dataset, train a Random Forest classifier, evaluate its accuracy and confusion matrix, and visualize decision boundaries and misclassifications using scikit‑learn and the dtreeviz library.

classificationdecision boundarydtreeviz

0 likes · 9 min read

Visualizing Random Forest Decision Boundaries on the Wine Dataset with dtreeviz

Tencent Cloud Developer

Dec 2, 2022 · Artificial Intelligence

Football Match Prediction Using Machine Learning and Betting Strategy Analysis

The study applies machine‑learning models—including logistic regression, SVM, random forest, deep neural networks and a DNN‑SVM ensemble—to 17‑dimensional team features and 51‑dimensional bookmaker odds, achieving up to 54.5% match‑outcome accuracy, proposing a profit‑condition betting strategy and extending the approach to stock‑price forecasting.

Betting StrategySVMSports Analytics

0 likes · 21 min read

Football Match Prediction Using Machine Learning and Betting Strategy Analysis

Didi Tech

May 11, 2021 · Artificial Intelligence

Continuous Causal Forest: Extending Uplift Modeling to Multi‑dimensional Continuous Treatments in Ride‑hailing Pricing

By extending binary causal forests with a Continuous Average Partial Effect statistic, the Continuous Causal Forest enables uplift modeling for multi‑dimensional continuous treatments such as ride‑hailing pricing, delivering superior Qini scores and over 15% ROI improvement while simplifying implementation and reducing deployment costs.

Causal InferenceRide-hailingcontinuous treatment

0 likes · 10 min read

Continuous Causal Forest: Extending Uplift Modeling to Multi‑dimensional Continuous Treatments in Ride‑hailing Pricing

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

Amap Tech

Jul 16, 2019 · Artificial Intelligence

Mobile Wi‑Fi Identification for Enhanced Network Positioning Using Machine Learning

By replacing rule‑based pipelines with an active‑learning‑driven random‑forest model that extracts clustering, signal, association, IP, and temporal features, Gaode accurately identifies mobile, cloned, and moved Wi‑Fi, cutting large‑error network‑positioning cases by ~18% and boosting overall positioning precision.

WiFi fingerprintingbig datamachine learning

0 likes · 13 min read

Mobile Wi‑Fi Identification for Enhanced Network Positioning Using Machine Learning

Qunar Tech Salon

Jan 17, 2019 · Artificial Intelligence

Introduction to scikit-learn for Machine Learning: Ensemble Learning – Random Forest Algorithm

This article provides a comprehensive introduction to the Random Forest algorithm, covering its theoretical background, scikit-learn implementation details, practical coding example with the Iris dataset, and a discussion of its advantages, limitations, and typical use cases in machine learning.

Pythonbaggingclassification

0 likes · 15 min read

Introduction to scikit-learn for Machine Learning: Ensemble Learning – Random Forest Algorithm

360 Zhihui Cloud Developer

Jan 30, 2018 · Operations

Can You Predict Switch Failures Before They Happen? Inside PreFix’s ML Approach

This article reviews the PreFix system, which uses machine‑learning on datacenter switch logs to predict hardware failures ahead of time, detailing its design, feature extraction, random‑forest model, experimental validation across multiple switch models, and its broader applicability to disk failure prediction.

Log Analysisdatacenter networksmachine learning

0 likes · 12 min read

Can You Predict Switch Failures Before They Happen? Inside PreFix’s ML Approach

Architects Research Society

Nov 17, 2016 · Artificial Intelligence

Comparative Study of Machine Learning Classifiers and Guidance for Algorithm Selection

The article summarizes a JMLR 2014 study that evaluated 179 classifiers across 121 UCI datasets, finding Random Forests and Gaussian‑kernel SVMs to be top performers, provides a review of supervised learning algorithms, and includes visual guidance for selecting appropriate machine‑learning methods.

SVMalgorithm selectionclassifier comparison

0 likes · 3 min read

Comparative Study of Machine Learning Classifiers and Guidance for Algorithm Selection

Architects Research Society

Oct 28, 2016 · Artificial Intelligence

Phishing Website Detection Using Machine Learning Models in R

This article presents a step‑by‑step machine‑learning analysis of the UCI Phishing Websites dataset in R, loading the data, training boosted logistic regression, SVM, tree‑bagging, and random‑forest models, comparing their accuracies, and identifying the most important predictive features for phishing detection.

RSVMcaret

0 likes · 11 min read

Phishing Website Detection Using Machine Learning Models in R

Qunar Tech Salon

Jul 4, 2016 · Information Security

Xiaomi Risk Control Practices: Architecture, Rule Engine, and Machine Learning

Xiaomi senior R&D engineer Deng Wenjun shares the evolution of Xiaomi's internet‑finance risk‑control system, describing early rule‑based limits, the adoption of Drools for fast rule deployment, data‑driven modeling with random‑forest classifiers, and ongoing challenges in scalability, latency, and privacy.

droolsfinancial technologymachine learning

0 likes · 16 min read

Xiaomi Risk Control Practices: Architecture, Rule Engine, and Machine Learning

High Availability Architecture

Jun 24, 2016 · Information Security

Xiaomi's Internet Finance Risk Control Practices: Architecture, Rules Engine, and Machine Learning

The article details Xiaomi's evolution of internet‑finance risk control—from early limit and frequency rules that cut bad‑debt by a third, through adopting the Drools rules engine for rapid deployment and gray‑release, to leveraging random‑forest machine‑learning models and extensive user profiling that reduced fraud by roughly 40%, while addressing privacy and operational challenges.

Xiaomidroolsinternet finance

0 likes · 15 min read

Xiaomi's Internet Finance Risk Control Practices: Architecture, Rules Engine, and Machine Learning