Answer / Shahnawaz Alam

Ensemble techniques (such as bagging, boosting, and stacking) combine multiple machine learning models to improve performance. One example of their use is in the field of image classification. By training several independent classifiers on different subsets of the same data or using various feature sets, an ensemble can achieve better accuracy than any individual model. For instance, a random forest (a bagging ensemble) might consist of hundreds of decision trees, each capturing a slightly different aspect of the underlying patterns in the images.

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