Random Forest

现代病虫害预警模型常用的建模方法包括时间序列分析、随机森林、LSTM神经网络

相比深度学习，梯度提升在中小型数据集上通常更稳定、更可解释、训练更快

Tree-based models (such as XGBoost and random forests) are relatively insensitive to encoding methods, whereas linear models and neural networks are highly dependent on proper encoding.

Random forests reduce variance and improve generalization through bagging, making them a classic representative of ensemble learning.

Random Forest was proposed by Leo Breiman in 2001 and is an ensemble learning method based on the Bagging idea.

When random forests were used on the main data-generating mechanism, the coverage of estimators using in-sample fitted propensity scores dropped to 0.12–0.48, severely under-covering; whereas after cross-fitting both nuisances, coverage recovered to 0.91–1.00.

AI和机器学习在微观模拟中的贡献包括：利用梯度提升、随机森林等算法更准确地预测个体对激励变化的反应，以及通过自然语言处理从非结构化文本中提取政策细节自动更新模型参数。

Random forests adopt a Bagging strategy, training multiple independent deep trees in parallel and merging them by voting or averaging, with the goal of reducing variance.

The study applied refined neutrosophic decomposition to an ensemble model composed of Random Forest, XGBoost, and Logistic Regression, outputting four mutually independent indicators: T-hat, F-hat, I1-hat, and I2-hat.

The effectiveness of ensemble diversity principles (such as random forests and boosting) is built on differentiation among base learners, and transferring this to multi-agent systems means introducing models with genuine differences in architecture, training data, or reasoning paths.

Common technical approaches for AI-driven root cause analysis include feature importance ranking using random forests or gradient boosting trees, LSTM networks for capturing temporal anomalies, and causal inference frameworks such as DoWhy.

RecipeDB2 uses a random forest classifier to extend 34 ingredient categories to the entire ingredient vocabulary.

Random Forest is a classic application of the Bagging concept; building on Bagging, it randomly selects √m features as candidates at each split, resulting in lower correlation among individual trees.

When the relationship between independent and dependent variables is nonlinear, pure linear regression may underfit, and polynomial features, interaction terms, or tree-based models can be considered.

Random forest classifiers generally exceed 90% accuracy on the KOI dataset after hyperparameter tuning.

The researchers built a discrete-event simulator covering three storage tiers—GPU HBM, CPU DRAM, and SSD—and calibrated it with a random forest execution-time predictor.

Core machine learning algorithms include linear regression, logistic regression, decision trees, random forests, gradient boosting (XGBoost, LightGBM), K-nearest neighbors, and K-means clustering.

A rough but complete baseline model (such as logistic regression or a random forest with default parameters) should be established as quickly as possible to verify that the data pipeline is bug-free and to provide a reference point for improvement.

The research uses three machine learning algorithms for training and evaluation: K-Nearest Neighbors (KNN), Random Forest (RF), and Support Vector Machine (SVM).

Random Forest achieved an F1 score of 0.97 on the IEEE-30 system and reached 0.86 on the IEEE-14 system.