For several organizations or data scientists that specialize in machine learning, ensemble learning techniques have become the weapons of preference. As ensemble learning methods connect various base models, together they have a higher capacity to provide a much more accurate ML model. For example, at Bigabid we have been ensemble learning to successfully solve many types of problems ranging from optimizing LTV (Customer Lifetime Value) to fraud exposure.
It is difficult not to overstate the value of ensemble learning to the overall ML process, including the bias-variance tradeoff and the three main ensemble techniques: bagging, boosting and stacking. These necessary procedures should be a piece of any data scientist’s tool kit, as they are theories that are encountered everywhere.
Ensemble Learning Methods: An Summary
Ensemble learning is an ML paradigm where there are many base models (which are often known as “weak learners”) are linked and trained to solve the identical problem. This method is based on the theory that by accurately mixing several base models together, these weak learners can be used as building blocks for creating more-complex ML models. Together these ensemble models ( also known as“strong learners”) produce better, more accurate results.
In other words, a weak learner is only a base model that solely performs rather poorly. In fact, its accuracy level is barely above chance, meaning that it predicts the result only somewhat better than a random guess would. These weak learners will often be computationally easy as well. Typically, the reason base models don’t work very well by themselves is because they either have high bias or too much variety, which makes them weak.
This is where ensemble learning comes in the scene. This system tries to overcome the general error by joining several weak learners together. Think of ensemble models as the data science version of the phrase “two heads are better than one.” If one model serves well, then a number of models working collectively can do even better.
A Statement about the Bias-Variance Tradeoff
It’s necessary to know the concept of a weak learner and why it got this name in the first place, as the reason gets down to either bias or variance. More particularly, the prediction error of an ML model, particularly the difference between the trained model and the ground truth, can be split down into the sum of the following: the bias and the variance. For instance:
- Error due to Bias: This is the variation between a model’s expected prediction and the specific value that we are trying to predict.
- Error due to Variance: This is the variance of a model prediction for a particular data point.
If a model is too simple and doesn’t have multiple parameters, then it may include high bias and low variance. In contrast, if a model has several parameters, then it may have high variance and low bias. As such, it’s important to find the best balance without underfitting or overfitting the data, as this tradeoff in complexity is the cause of why there exists a tradeoff between variance and bias. Directly put, an algorithm can’t simultaneously be more complex and less complex in the same period.
Ensemble Learning Techniques – Combining Weak Learners
There are three important ensemble techniques: bagging, boosting and stacking. There are described as follows:
Bagging tries to incorporate related learners on small-sample groups and measures the average of all the predictions. Usually, bagging permits you to use various learners in various groups. By doing so, this method helps to decrease the variance error.
Boosting is an iterative technique that fine-tunes the weight of research according to the most current classification. If an investigation was incorrectly classified, this approach will increase the weight of that research in the next round and will be limited inclined to misclassification. Similarly, if the research was classified correctly, then it will decrease its weight for the upcoming classifier. The weight describes how essential the correct classification of the specific data point should be, as this allows the sequential classifiers to concentrate on examples earlier misclassified. Generally, boosting decreases the bias error and makes strong predictive models, but at moments they may overfit on the training data.
Stacking is a smart way of linking the information given by different models. With this method, a learner of any sort can be used to connect different learners’ outputs. The outcome can be a decrease in bias or variance determined by which linked models are used.
The Promise of Ensemble Learning
Ensemble learning is about joining multiple base models to produce a more efficient and reliable ensemble model that features more powerful features and thus, performs more reliable. Ensemble learning methods have successfully set record performances on challenging datasets and are regularly a part of the winning submissions of Kaggle competitions.
It justifies noting that as with the three main ensemble techniques – bagging, boosting and stacking – variants are still possible and can be better planned to more efficiently adapt to specific queries, such as classifications, regression, time-series analyses, etc. This first need an understanding of the obstacle at hand and to be creative in approaching problem-solving!