Voting Ensemble Model

A voting ensemble combines the predictions of several different models, by majority vote (hard voting), averaged predicted probabilities (soft voting), or averaged values for regression, to produce one final prediction.

투표 앙상블은 여러 다른 모형의 예측을 다수결(하드 보팅), 예측확률 평균(소프트 보팅), 또는 회귀의 경우 예측값 평균으로 결합하여 하나의 최종 예측을 만드는 방법입니다.

How do I combine several different machine learning models into one prediction?

서로 다른 머신러닝 모형 여러 개를 하나의 예측으로 어떻게 결합하나요?

A voting ensemble trains several distinct models, such as logistic regression, random forest, and gradient boosting, and combines their predictions by majority vote or by averaging predicted probabilities. It fits when the base models are reasonably accurate but make different errors, which often makes the combined prediction more stable than any single model.

When to use Voting Ensemble Model

• Several reasonably accurate classifiers or regressors that make different kinds of errors
• Labelled tabular data with an outcome and multiple predictors
• Seeking more stable predictions without a complex meta-model

When to use something else

• Learning how to weight models from data: use a stacking ensemble
• A single interpretable model is required: use logistic regression or a decision tree

Assumptions and requirements

• Labelled training data
• Diverse base models with better-than-chance performance
• Reasonably calibrated probabilities for soft voting
• Performance evaluated with cross-validation or a held-out test set

What the analysis reports

• Cross-validated performance of the ensemble and each base model
• Accuracy, AUC, F1, or RMSE as appropriate
• Confusion matrix for classification
• Model weights if weighted voting is used

How to report it

The soft-voting ensemble achieved a cross-validated AUC of [value] (SD [value]), compared with [value] for the best single base model.

Related methods

• Stacking Ensemble
• Random Forest
• Gradient Boosting

Run Voting Ensemble Model on Quorant