Ensemble methods use multiple learning algorithms to obtain better predictive performance than could be obtained from any of the constituent learning algorithms alone. (Wikipedia)
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.
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.
Evaluation metrics to analyze the performance of models Industry relevance of linear and logistic regression Mathematics behind KNN, SVM and Naive Bayes algorithms Implementation of KNN, SVM and Naive Bayes using sklearn Attribute selection methods- Gini Index and Entropy Mathematics behind Decision trees and random forest Boosting algorithms:- Adaboost, Gradient Boosting and XgBoost Different Algorithms for Clustering Different methods to deal with imbalanced data Correlation Filtering Content and Collaborative based filtering Singular Value Decomposition Different algorithms used for Time Series forecasting Hands on Real-World examples. To make sense out of this course, you should be well aware of linear algebra, calculus, statistics, probability and python programming language. To make sense out of this course, you should be well aware of linear algebra, calculus, statistics, probability and python programming language. This course is a perfect fit for you. This course will take you step by step into the world of Machine Learning.
A new research project has found that the discretionary decisions made by human bank managers can be replicated by machine learning systems to an accuracy of more than 95%. Using the same data available to bank managers in a privileged dataset, the best-performing algorithm in the test was a Random Forest implementation – a fairly simple approach that's twenty years old, but which still outperformed a neural network when attempting to mimic the behavior of human bank managers formulating final decisions about loans. The Random Forest algorithm, one of four put through their paces for the project, achieves high human-equivalent scoring vs. performance of bank managers, despite the relative simplicity of the algorithm. The researchers, who had access to a proprietary dataset of 37,449 loan ratings across 4,414 unique customers at'a large commercial bank', suggest at various points in the preprint paper that the automated data analysis that managers are given to make their decision has now become so accurate that bank managers rarely deviate from it, potentially signifying that bank managers' part in the loan approval process chiefly consists of retaining someone to fire in the event of a loan default. 'From a practical perspective it is worth noting that our results may indicate that the bank could process loans faster and cheaper in the absence of human loan managers with very comparable results.