AdaBoost Algorithm is a boosting method that works by combining weak learners into strong learners. A good way for a prediction model to correct its predecessor is to give more attention to the training samples where the predecessor did not fit well. This can result in a new prediction model which will focus much on the hard instances. This technique is used by an AdaBoost Algorithm. In this article, I will take you through the AdaBoost Algorithm in Machine Learning.

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This article is motivated by the problem of selecting the functional form of trees and ensemble size in gradient tree boosting (Friedman, 2001; Mason et al., 2000). Gradient tree boosting (GTB) has become extremely popular in recent years, both in academia and industry: At present, an increase in the size of datasets, both in the number of observations and the richness of the data, or number of features, is seen. This, coupled with an exponential increase in computational power and a growing revelation and acceptance for data-driven decisions in the industry makes for an increasing interest in statistical learning (Hastie et al., 2001). For these new datasets, standard statistical methods such as generalized linear models (McCullagh and Nelder, 1989) that have a fixed learning rate due to their constrained functional form with bounded complexity, struggle in terms of predictive power, as they stop learning at certain information thresholds. The interest is therefore geared towards more flexible approaches such as ensembles of learners.

XGBoost is a popular machine learning library designed specifically for training decision trees and random forests. For information about installing XGBoost on Databricks Runtime, or installing a custom version on Databricks Runtime ML, see these instructions. You can train XGBoost models on an individual machine or in a distributed fashion.

Ensemble methods is a machine learning technique that combines several base models in order to produce one optimal predictive model. There are various ensemble methods such as stacking, blending, bagging, and boosting. Gradient Boosting, as the name suggests is a boosting method. Boosting is loosely-defined as a strategy that combines multiple simple models into a single composite model. With the introduction of more simple models, the overall model becomes a stronger predictor.

Random forests, originally introduced by Breiman [8], constitute an ensemble learning algorithm for classification and regression, which produces predictions by first growing a large number of randomized decision trees [9] and, then, aggregates the results. Since its introduction, the algorithm has been applied in various fields such as object recognition [25], bioinformatics [12], ecology [10, 22] and finance [15, 18], and the evidence is strong: with very little tuning, random forests are able to deliver a flexible tool for prediction which is fully comparable with other state-of-the-art algorithms. In fact, Howard and Bowles [17] claim that random forests have been the most successful general-purpose algorithm in recent times. While many successful applications indicate the wide applicability of random forests, only little theoretical work exists to support this impression.

Ensemble techniques--wherein a model is composed of multiple (possibly) weaker models--are prevalent nowadays within the field of machine learning (ML). Well-known methods such as bagging [1], boosting [2], and stacking [3] are ML mainstays, widely (and fruitfully) deployed on a daily basis. Generally speaking, there are two types of ensemble methods, the first generating models in sequence--e.g., AdaBoost [2]--the latter in a parallel manner--e.g., random forests [4] and evolutionary algorithms [5]. AdaBoost (Adaptive Boosting) is an ML meta-algorithm that is used in conjunction with other types of learning algorithms to improve performance. The output of so-called "weak learners" is combined into a weighted sum that represents the final output of the boosted classifier.

When I started my Data Science journey,few terms like ensemble,boosting often popped up.Whenever I opened the discussion forum of any Kaggle Competition or looked at any winner's solution,it was mostly filled with these things. At first these discussions sounded totally alien,and these class of ensemble models looked like some fancy stuff not meant for the newbies,but trust me once you have a basic understanding behind the concepts you are going to love them! So let's start with a very simple question,What exactly is ensemble? "A group of separate things/people that contribute to a coordinated whole" In a way this is kind of the core idea behind the entire class of ensemble learning! Well let's rewind the clocks a bit and go back to the school days for a while, remember you used to get a report card with an overall grade.Well how exactly was this overall grade calculated,your teachers of respective subjects gave some feedback based on their set of criteria,for example your math teacher would assess you on his own criteria like algebra,trigonometry etc, sports teacher would judge you how you perform on the field,your music teacher would judge on you vocal skills.Point being each of these teachers have their own set of rules of judging the performance of a student and later all of these are combined to give an overall grade on the performance of the student.

This post is about LSBoost, an Explainable'AI' algorithm which uses Gradient Boosted randomized networks for pattern recognition. In LSBoost, more specifically, the so called weak learners from LS_Boost are based on randomized neural networks' components and variants of Least Squares regression models. I've already presented some promising examples of use of LSBoost based on Ridge Regression weak learners. In mlsauce's version 0.7.1, the Lasso can also be used as an alternative ingredient to the weak learners. Here is a comparison of the regression coefficients obtained by using mlsauce's implementation of Ridge regression and the Lasso: The following example is about training set error vs testing set error, as a function of the regularization parameter, both for Ridge regression and Lasso-based weak learners.

Disclaimer: I'm a Senior Data Scientist at Saturn Cloud -- we make enterprise data science fast and easy with Python, Dask, and RAPIDS. Check out a video walkthrough here. Random forest is a machine learning algorithm trusted by many data scientists for its robustness, accuracy, and scalability. The algorithm trains many decision trees through bootstrap aggregation, then predictions are made from aggregating the outputs of the trees in the forest. Due to its ensemble nature, random forest is an algorithm that can be implemented in distributed computing settings.