The technique of ensemble learning in machine learning combines the predictions from multiple models to improve performance. Ensemble learning’s core concept is that multiple models working together can achieve greater accuracy and generalization. This approach is gaining popularity both in academic and industrial applications because it reduces errors, minimizes overfitting and increases robustness.
Concept of Ensemble Learning
Ensemble learning is based on the concept that different models can have different strengths and weakness. Ensemble learning is able to capture more information and reduce errors by combining predictions. This is especially useful for complex tasks, such as image classifying, natural language processing and anomaly detection where a single algorithm may not be able to capture all patterns.
The “wisdom” of the crowd is used to guide ensemble learning, where different opinions can lead to better decisions. Combining multiple models with different perspectives can result in a more accurate and balanced prediction.
There are many types of ensemble learning techniques
The implementation of ensemble learning is possible using a variety of techniques. Each technique has its own unique way to combine multiple models. Bagging, boosting and stacking are the most common types.
Bagging
Bagging, also known as Bootstrap Aggregating or Bagging, is a technique in which multiple instances of a model are trained using different subsets created by bootstrapping. The final prediction is based on averaging or majority voting. Random Forest, a bagging technique that uses decision tree base models, is a common example.
Bagging helps reduce variances by averaging out the biases of individual models. This results in a model that is more robust and stable, which can generalize better to unknown data. Bagging reduces the risk that the model will overfit by training it on different subsets. It also ensures that diverse patterns are captured in the data.
Boosting
Boosting is a powerful technique for ensemble learning that improves weak learners in a sequential manner. In boosting, the models are taught in sequence. Each model learns from its predecessor’s mistakes. This process is repeated until a robust predictive model has been formed.
AdaBoost is a popular boosting algorithm, as are Gradient Boosting and XGBoost. By focusing on difficult-to-predict cases, boosting reduces bias and variance. This allows the model to improve its accuracy and correct any mistakes. This technique is used widely in production and competitive machine-learning tasks.
Stacking
Combining predictions from several base models is called stacking. A meta-model is used to combine the predictions. The base models learn from the original dataset and then feed their predictions into a metamodel that will learn how to combine these predictions optimally. This technique allows you to combine the strengths of various types of models in order to improve performance.
Stacking uses a variety of base models, such as support vector machines (SVMs), neural networks, and decision trees. The meta-model is a final judge that determines which model’s predictions to trust in certain scenarios. This approach improves the model’s performance by capturing patterns in data.
Voting
Voting is an easy ensemble learning technique in which multiple models predict and the final prediction can be obtained by majority voting (for classification), or average (for regression). This method is especially useful when combining different models, such as support vector machines, decision trees, and logistic regression.
Soft voting is based on probabilities, not on a majority vote. Soft voting is generally more effective as it takes into account the level of confidence in each model’s predictions.
Ensemble learning improves model performance
The performance of models can be improved in many ways by ensemble learning. These include reducing variance, minimising bias, increasing robustness and improving generalization. We will explore these benefits in more detail.
Reduce Variance
The term “variance” refers to a model’s sensitivity towards fluctuations in the data used for training. Models with high variance, like decision trees, are more likely to be overfitted by the training data, and therefore perform poorly when dealing with unknown data. Ensemble learning, notably through bagging reduces variance by averaging predictions from multiple models. This stabilizes the model, and makes it more generalizable to new data.Data Science Training in Pune