Ensemble methods use multiple learning algorithms to obtain better predictive performance than could be obtained from any of the constituent learning algorithms alone. (Wikipedia)
A random forest is a popular tool for estimating probabilities in machine learning classification tasks. However, the means by which this is accomplished is unprincipled: one simply counts the fraction of trees in a forest that vote for a certain class. In this paper, we forge a connection between random forests and kernel regression. This places random forest probability estimation on more sound statistical footing. As part of our investigation, we develop a model for the proximity kernel and relate it to the geometry and sparsity of the estimation problem. We also provide intuition and recommendations for tuning a random forest to improve its probability estimates.
Random Forest (RF) is an ensemble supervised machine learning technique that was developed by Breiman over a decade ago. Compared with other ensemble techniques, it has proved its superiority. Many researchers, however, believe that there is still room for enhancing and improving its performance accuracy. This explains why, over the past decade, there have been many extensions of RF where each extension employed a variety of techniques and strategies to improve certain aspect(s) of RF. Since it has been proven empirically that ensembles tend to yield better results when there is a significant diversity among the constituent models, the objective of this paper is twofold.
Ensemble is a machine learning concept in which multiple models are trained using the same learning algorithm. Bagging is a way to decrease the variance in the prediction by generating additional data for training from dataset using combinations with repetitions to produce multi-sets of the original data. Boosting is an iterative technique which adjusts the weight of an observation based on the last classification. If an observation was classified incorrectly, it tries to increase the weight of this observation. Boosting in general builds strong predictive models.
Machine Learning is a fast evolving field – but a few things would remain as they were years ago. One such thing is ability to interpret and explain your machine learning models. If you build a model and can not explain it to your business users – it is very unlikely that it will see the light of the day. Can you imagine integrating a model into your product without understanding how it works? Or which features are impacting your final result? In addition to backing from stakeholders, we as data scientists benefit from interpreting our work and improving upon it. The first article of this fast.ai I'm delighted to share part 2 of this series, which primarily deals with how you can intepret a random forest model.
In this paper we present CatBoost, a new open-sourced gradient boosting library that successfully handles categorical features and outperforms existing publicly available implementations of gradient boosting in terms of quality on a set of popular publicly available datasets. The library has a GPU implementation of learning algorithm and a CPU implementation of scoring algorithm, which are significantly faster than other gradient boosting libraries on ensembles of similar sizes.
This article provides a comprehensive study of different ways to make speed benchmarks of gradient boosted decision trees (GBDT) algorithm. We show main problems of several straight forward ways to make benchmarks, explain, why a speed benchmark is challenging task and provide a set of reasonable requirements for a benchmark to be fair and useful.
Existing guarantees in terms of rigorous upper bounds on the generalization error for the original random forest algorithm, one of the most frequently used machine learning methods, are unsatisfying. We discuss and evaluate various PAC-Bayesian approaches to derive such bounds. The bounds do not require additional hold-out data, because the out-of-bag samples from the bagging in the training process can be exploited. A random forest predicts by taking a majority vote of an ensemble of decision trees. The first approach is to bound the error of the vote by twice the error of the corresponding Gibbs classifier (classifying with a single member of the ensemble selected at random). However, this approach does not take into account the effect of averaging out of errors of individual classifiers when taking the majority vote. This effect provides a significant boost in performance when the errors are independent or negatively correlated, but when the correlations are strong the advantage from taking the majority vote is small. The second approach based on PAC-Bayesian C-bounds takes dependencies between ensemble members into account, but it requires estimating correlations between the errors of the individual classifiers. When the correlations are high or the estimation is poor, the bounds degrade. In our experiments, we compute generalization bounds for random forests on various benchmark data sets. Because the individual decision trees already perform well, their predictions are highly correlated and the C-bounds do not lead to satisfactory results. For the same reason, the bounds based on the analysis of Gibbs classifiers are typically superior and often reasonably tight. Bounds based on a validation set coming at the cost of a smaller training set gave better performance guarantees, but worse performance in most experiments.
Gradient boosting decision trees is the state of the art for structured data problems. Two modern algorithms that make gradient boosted tree models are XGBoost and LightGBM. In this article I'll summarize their introductory papers for each algorithm's approach. Gradient Boosting Decision Trees (GBDT) are currently the best techniques for building predictive models from structured data. Unlike models for analyzing images (for that you want to use a deep learning model), structured data problems can be solved very well with a lot of decision trees.
The Random Forest is an extremely popular machine learning algorithm. Often, with not too much pre-processing, one can throw together a quick and dirty model with no hyperparameter tuning and acheive results that aren't awful. As an example, I put together a RandomForestRegressor in Python using scikit-learn for the New York City Taxi Fare Prediction playground competition on Kaggle recently, passing in no arguments to the model constructor and using 1/100 for the training data (554238 of 55M rows), for a validation R² of 0.8. NOTE: This snippet assumes you split the data into training and validation sets with your features and target variable separated. You can see the full code on my GitHub profile.