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The choice between training an ensemble of models on separate portions of the dataset versus training a single model on the entire dataset is nuanced and may depend on the specific characteristics and requirements of your project. Here are some considerations based on various sources:
Data Subgroups
If your data has subgroups with distinct characteristics, splitting the dataset and training separate models on each chunk might be beneficial. Each model in the ensemble can potentially learn the unique characteristics of its subset of the data. However, if the data doesn’t have significant subgroups, it might be more advantageous to train a single model on the entire dataset to capture the overall distribution of the data.
Accuracy and Bias
A single model might not be accurate enough or may exhibit bias towards certain features. In such cases, ensemble models can help improve accuracy and mitigate bias. They can aggregate the predictions from multiple models, each trained on a different subset of the data, to make a more balanced and potentially more accurate final prediction2.
Bagging
Bootstrap Aggregation (Bagging) is a technique where each model in the ensemble is trained on a different sample of the training dataset. This can be extended to training models on different chunks of the dataset, which might help in reducing overfitting and improving the model’s robustness to data variability.
Diverse Predictions
Ensemble modeling can create multiple diverse models to predict an outcome either by using different modeling algorithms or different training datasets. This diversity can help in achieving better performance and robustness in predictions4.
Large Datasets
In the case of large datasets, dividing the dataset into two or more subsets and training base models on these subsets can be a procedure to create an ensemble model. This might be a strategy to handle large datasets and to build models that can capture different aspects of the data5.
It may be beneficial to experiment with both approaches — training an ensemble of models on separate data chunks and a single model on the entire dataset — to observe which strategy yields better performance in your specific scenario. It’s also crucial to consider the computational resources and time available, as training multiple models could be more resource-intensive.
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