Gradient descent is an optimization algorithm that follows the negative gradient of an objective function in order to locate the minimum of the function. A limitation of gradient descent is that it uses the same step size (learning rate) for each input variable. AdaGrad, for short, is an extension of the gradient descent optimization algorithm […]
Dual Annealing is a stochastic global optimization algorithm. It is an implementation of the generalized simulated annealing algorithm, an extension of simulated annealing. In addition, it is paired with a local search algorithm that is automatically performed at the end of the simulated annealing procedure. This combination of effective global and local search procedures provides […]
The Broyden, Fletcher, Goldfarb, and Shanno, or BFGS Algorithm, is a local search optimization algorithm. It is a type of second-order optimization algorithm, meaning that it makes use of the second-order derivative of an objective function and belongs to a class of algorithms referred to as Quasi-Newton methods that approximate the second derivative (called the […]
Bootstrap aggregation, or bagging, is a popular ensemble method that fits a decision tree on different bootstrap samples of the training dataset. It is simple to implement and effective on a wide range of problems, and importantly, modest extensions to the technique result in ensemble methods that are among some of the most powerful techniques, […]
Ensemble learning combines the predictions from machine learning models for classification and regression. We pursue using ensemble methods to achieve improved predictive performance, and it is this improvement over any of the contributing models that defines whether an ensemble is good or not. A property that is present in a good ensemble is the diversity […]
An ensemble learning method involves combining the predictions from multiple contributing models. Nevertheless, not all techniques that make use of multiple machine learning models are ensemble learning algorithms. It is common to divide a prediction problem into subproblems. For example, some problems naturally subdivide into independent but related subproblems and a machine learning model can […]
Boosting is a powerful and popular class of ensemble learning techniques. Historically, boosting algorithms were challenging to implement, and it was not until AdaBoost demonstrated how to implement boosting that the technique could be used effectively. AdaBoost and modern gradient boosting work by sequentially adding models that correct the residual prediction errors of the model. […]
Ensemble Learning Algorithms With Python Crash Course. Get on top of ensemble learning with Python in 7 days. Ensemble learning refers to machine learning models that combine the predictions from two or more models. Ensembles are an advanced approach to machine learning that are often used when the capability and skill of the predictions are […]
Weighted average ensembles assume that some models in the ensemble have more skill than others and give them more contribution when making predictions. The weighted average or weighted sum ensemble is an extension over voting ensembles that assume all models are equally skillful and make the same proportional contribution to predictions made by the ensemble. […]
It is common to describe ensemble learning techniques in terms of weak and strong learners. For example, we may desire to construct a strong learner from the predictions of many weak learners. In fact, this is the explicit goal of the boosting class of ensemble learning algorithms. Although we may describe models as weak or […]
