What are model ensemble techniques and when would you use them?

Model ensemble techniques involve combining multiple models to improve overall performance compared to individual models. Common methods include bagging, boosting, and stacking. These techniques are used when individual models exhibit high variance, low bias, or when there is a need to leverage diverse modeling approaches to capture different patterns in data. For instance, boosting iteratively adjusts the learning process to focus on errors from previous models, making it useful in scenarios where accuracy is paramount.