Was sind die Schritte zur Verwendung von Ensembling zur Verbesserung der Leistung von Machine Learning-Modellen?

Consider ensembling methods as tools in a toolbox—each has its purpose. Bagging is like having a team of diverse experts who collaborate and average their opinions, smoothing out individual biases. On the other hand, boosting is akin to a mentorship program where each "mentor" corrects the mistakes of the previous one, refining the overall skillset. It's not just about reducing errors; it's about leveraging the strengths of different approaches to enhance the overall predictive power.

Ensembling, the darling of ML world, has garnered a rep. for its performance-boosting capabilities. However, it's essential to approach this technique with a discerning eye. It is only good as the data quality. First overlooked step is data prep. If ur dataset is riddled with errors, missing values, or inconsistencies, no amount of ensembling can save u. Ensembling adds complexity. As u combine multiple models, u introduce more parameters & potential points of failure. Each model requires careful tuning & hyperparameter optimisation. The intricate dance of managing parameters can be overwhelming. Combining predictions can lead to data leakage. If ur models have somehow memorised the training data, ensembling won't save u from overfitting.

Embarking on model ensembling is pivotal for predictive prowess. Bagging, creating models from diverse data subsets and averaging predictions, reduces variance, enhancing stability. Boosting, a sequential model creation refining errors, trims bias, boosting accuracy. The choice hinges on problem intricacies and dataset nuances. Bagging tames variance, shielding against fluctuations. Boosting, a beacon of bias reduction, fine-tunes accuracy iteratively. In this ML landscape, the strategic use of bagging and boosting is not a preference but a tailored approach sculpted by your unique challenge.

Depending on the data distribution, some models might fit more on some parts of it than others. Let’s say one feature of your data is defined on [a; b], with c a value in ]a;b[. The data on ]a;c] might male a model fit really well (let’s say it’s a linear distribution, whereas it might be awful on ]c;b] (for example with a quadratic distribution). Therefore, data exploration is a really important step in choosing the models.

Combining models can be done either by sequencing the space, or by combining the outputs of several models. In the first case, defining the intervals for each models is the way to go. In the second one, try different weighting for the output combination, to have a function like f(input) = a * g(input) + b * h(input), with g and h two models and a and b the weights. You can even go further by feed a neural network with the outputs of each models, adding then another learning step to your process.

Ensemble models like xgboost and lightgbm are one of the best for training machine learning models on tabular data, as they are more able to catch the complexity by having multiples specialised models. Ensembling multiple machine learning models like SVM, Logistic Regression, XGboost can help to reach even better performance score by catching even more data complexity. The main challenges of ensembling are that it consumes a lot of compute ressources to train, infer multiples models, and that it increases latency. Ensemble models allowing compute distribution on multi cores and machines can help reduce the model latency.

The advantages are quiet clear: it allows to play with different models complexities and specificities to better fit your data. Most of the challenges have been discussed above, but to sum up finding a balance between each model. However, i am pretty sure based on my experience that using a neural network (even simple, one FC layer for instance) can reduce the work on this task. The other challenge is picking the right base models, but for now the best solution in my opinion is combining data analysis, experience, and experiments to find them.

Hyperparameter optimization is crucial when it comes to ensembling. Every base model has its unique set of hyperparameters which can be tuned to achieve optimal performance. Some of these hyperparameters include depth, minimum number of samples per leaf, etc. Optimizing hyperparameters for each model in an ensemble can significantly enhance its performance, leading to better predictions overall. Ensembling techniques also have their own hyperparameters, such as the learning rate in gradient boosting and combining methods in stacking. Tuning hyperparameters of both individual models and ensembling techniques are vital to achieving the best results.

With the ensemble technique, can the ensemble model overfit to the training data? Well, if you are employing boosting methods to build your ensembles, if your starting point has already been overfitted to the training data the subsequent models will not be able to correct that. Hence, for ensembles it's crucial that each model/learner are low performing, they should not be too powerful to overfit, and let the ensemble be powerful. The learners in ensembles are called weak learners. Which is desirable.