Ensemble methods use multiple learning algorithms to obtain better predictive performance than could be obtained from any of the constituent learning algorithms alone. (Wikipedia)
The design and optimization of new processing approaches for the development of rare earth cuprate (REBCO) high temperature superconductors is required to increase their cost-effective fabrication and promote market implementation. The exploration of a broad range of parameters enabled by these methods is the ideal scenario for a new set of high-throughput experimentation (HTE) and data-driven tools based on machine learning (ML) algorithms that are envisaged to speed up this optimization in a low-cost and efficient manner compatible with industrialization. In this work, we developed a data-driven methodology that allows us to analyze and optimize the inkjet printing (IJP) deposition process of REBCO precursor solutions. A dataset containing 231 samples was used to build ML models. Linear and tree-based (Random Forest, AdaBoost and Gradient Boosting) regression algorithms were compared, reaching performances above 87%.
Problem Statement A target marketing campaign for a bank was undertaken to identify a segment of customers who are likely to respond to an insurance product. Here, the target variable is whether or not the customers bought insurance product and it depends on factors like Product usage in three months, demographics, transaction patterns as like deposit amount, checking account, a branch of the bank, Residential information (like urban, rural) and so on.
Random Forest is a supervised machine learning algorithm that is composed of individual decision trees. This type of model is called an ensemble model because an "ensemble" of independent models is used to compute a result. The basis for the Random Forest is formed by many individual decision trees, the so-called Decision Trees. A tree consists of different decision levels and branches, which are used to classify data. The Decision Tree algorithm tries to divide the training data into different classes so that the objects within a class are as similar as possible and the objects of different classes are as different as possible. This tree helps to decide whether to do sports outside or not, depending on the weather variables "weather", "humidity" and "wind force".
Recently I came across this incredible survey paper on the use of neural networks for tabular data. After going through it carefully, I can confidently say that it's thus far THE best paper on the subject. It goes into depth of all the main issues that have stymied the use of NNs in this domain. The paper is very thoughtful, systematic, and fairly thorough. Despite what the authors claim, though, it is not the first paper on the topic, but it goes well beyond many recent papers on the subjects. It also doesn't have as an exhaustive set of datasets that it uses as some of the other papers.
Last week I published two articles about Decision Trees: one about Decision and Classification Tree (CART) and another tutorial on how to implement Random Forest classifier. These two methods may look very similar, however there are important differences that every data professional or enthusiastic should know.
Ensemble methods are well established as an algorithmic cornerstone in machine learning (ML). Just as in real life, in ML a committee of experts will often perform better than an individual provided appropriate care is taken in constituting the committee. Since the earliest days of ML research, a variety of ensemble strategies have been developed with random forests and gradient boosting emerging as leading-edge methods in classification today. It has been recognised since the early days of ML research that ensembles of classifiers can be more accurate than individual models. In ML, ensembles are effectively committees that aggregate the predictions of individual classifiers. They are effective for very much the same reasons a committee of experts works in human decision making, they can bring different expertise to bear and the averaging effect can reduce errors. This article presents a tutorial on the main ensemble methods in use in ML with links to Python notebooks and datasets illustrating these methods in action. The objective is to help practitioners get started with ML ensembles and to provide an insight into when and why ensembles are effective. There have been a lot of developments since then and the ensemble idea is still to the forefront in ML applications. For example, random forests  and gradient boosting  would be considered among the most powerful methods available to ML practitioners today. The generic ensemble idea is presented in Figure 1. All ensembles are made up of a collection of base classifiers, also known as members or estimators.
Machine learning models developed to predict energy properties of torrefied biomass. Collaborative game theory adopted to aid interpretability of key variables in torrefaction. Gradient boosting offered the highest prediction accuracy with 22-feature input. Novel framework to explain local and global effects of each feature on torrefaction. Torrefaction is a treatment process for converting biomass to high-quality solid fuels.
A few weeks ago, I wrote an article demonstrating random forest classification models. In this article, we will demonstrate the regression case of random forest using sklearn's RandomForrestRegressor() model. Similarly to my last article, I will begin this article by highlighting some definitions and terms relating to and comprising the backbone of the random forest machine learning. The goal of this article is to describe the random forest model, and demonstrate how it can be applied using the sklearn package. Our goal will not be to solve for the most optimal solution as this is just a basic guide.