Building learning models with Ensemble Learning

When we select a model, the most commonly used procedure is to choose the one with the smallest error on the training dataset. The problem with this approach is that it will not always work. The model might get biased or overfit the training data. Even when we compute the model using cross-validation, it can perform poorly on unknown data. One of the main reasons ensemble learning is so effective is because it reduces the overall risk of making a poor model selection. This enables it to train in a diverse manner and then perform well on unknown data. When we build a model using ensemble learning, the individual models need to exhibit some diversity. This would allow them to capture various nuances in our data; hence the overall model becomes more accurate. The diversity is achieved by using different training parameters for each individual model. This allows individual models to generate different decision boundaries for training data. This means that each model will use different rules to make an inference, which is a powerful way of validating the final result. If there is agreement among the models, then we know that the output is correct.