Introduction to Ensemble Learning

Ensemble learning is a powerful technique in machine learning that combines multiple individual models to create a stronger and more accurate predictive model. It enhances the performance of the model by aggregating predictions from different models, allowing us to overcome the limitations of individual models and improve overall performance. In this lesson, we’ll explore the concept of ensemble learning, understand its importance, and discover practical applications of this technique.

What is ensemble learning?

Ensemble learning involves training multiple models on the same dataset and combining their predictions to make final predictions. Each model in the ensemble is trained independently using different algorithms or variations of the same algorithm. The ensemble then combines the predictions of these models in various ways, such as averaging, voting, or weighted voting, to make the final prediction. The idea behind ensemble learning is that by combining diverse models, we can reduce biases, increase robustness, and achieve better generalization.

Why ensemble learning?

For a better understanding of the need for ensemble learning, let’s assume that each model, out of a total of $n$ models, makes an error with a probability of $p$. Since the models are independent, the probability of a majority of models making an error can be calculated as follows:

$$\begin{align*} p_k &= {n \choose k}.p^k.(1-p)^{n-k} \\ p_{m} &= \sum_{k=\frac{n}{2}+1}^{n} {n \choose k}.p^k.(1-p)^{n-k} \\ \end{align*}$$ ...