Ensemble Learning: Data Science.

Ensemble Learning is a technique or process in which multiple models are generated and combined to solve a particular machine learning problem. Ensemble Learning is meta-algorithms that combine multiple models to try and solve the same problem. It is primarily used to improve the performance of a model and reduce the variance of the outcome. Choosing which model to use is extremely important in any regression or classification problem and the choice depends on many variables such as the quantity of data, distribution of data, and its types. In supervised machine learning an algorithm creates a model from training data with the goal to best estimate the output variable (y) given the data (X).