Modern pattern recognition tasks use complex algorithms that take advantage of large datasets to make more accurate predictions than traditional algorithms such as decision trees or k-nearest-neighbor better suited to describe simple structures. While increased accuracy is often crucial, less complexity also has value. This paper proposes a training data selection algorithm that identifies multiple subsets with simple structures. A learning algorithm trained on such a subset can classify an instance belonging to the subset with better accuracy than the traditional learning algorithms. In other words, while existing pattern recognition algorithms attempt to learn a global mapping function to represent the entire dataset, we argue that an ensemble of simple local patterns may better describe the data. Hence the sub-setting algorithm identifies multiple subsets with simple local patterns by identifying similar instances in the neighborhood of an instance. This motivation has similarities to that of gradient boosted trees but focuses on the explainability of the model that is missing for boosted trees. The proposed algorithm thus balances accuracy and explainable machine learning by identifying a limited number of subsets with simple structures. We applied the proposed algorithm to the international stroke dataset to predict the probability of survival. Our bottom-up sub-setting algorithm performed on an average 15% better than the top-down decision tree learned on the entire dataset. The different decision trees learned on the identified subsets use some of the previously unused features by the whole dataset decision tree, and each subset represents a distinct population of data.

This course is a perfect fit for you. This course will take you step by step into the world of Machine Learning. Machine Learning is the study of computer algorithms that automates analytical model building. It is a branch of Artificial Intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention. Machine Learning is actively being used today, perhaps in many more places than one world expects.

If you've heard of "random forests" as a hot, sexy machine learning algorithm and you want to implement it, great! But if you're not sure exactly what happens in a random forest, or how random forests make their classification decisions, then read on:) We'll find that we can break down random forests into smaller, more digestible pieces. As a forest is made of trees, so a random forest is made of a bunch of randomly sampled sub-components called decision trees. So first let's try to understand what a decision tree is, and how it comes to its prediction. For now, we'll just look at classification decision trees.

It is meant in a way that we generate multiple new training data for statistical forecasting methods like ARIMA or triple exponential smoothing (Holt-Winters method etc.) to improve forecasting accuracy. It is called bootstrapping, and after applying the forecasting method on each new time series, forecasts are then aggregated by average or median – then it is bagging – bootstrap aggregating. It is proofed by multiple methods, e.g. in regression, that bagging helps improve predictive accuracy – in methods like classical bagging, random forests, gradient boosting methods and so on. The bagging methods for time series forecasting were used also in the latest M4 forecasting competition. For residential electricity consumption (load) time series (as used in my previous blog posts), I proposed three new bootstrapping methods for time series forecasting methods.

In this article, we'll learn about XGBoost, its background, its widely accepted usage in competitions such as Kaggle's and help you build an intuitive understanding of it by diving into the foundation of this algorithm. XGBoost is an algorithm that is highly flexible, portable, and efficient which is based on a decision tree for ensemble learning for Machine Learning that uses the distributed gradient boosting framework. Machine Learning algorithms are implemented with XGBoost under the Gradient boosting framework. XGBoost is capable of solving data science problems accurately in a short duration with its parallel tree boosting which is also called Gradient Boosting Machine (GBM), Gradient Boosting Decision Trees (GBDT). It is extremely portable and cross-platform enabled such that the very same code can be run on the different major distributed environments such as Hadoop, MPI, and SGE and enables solving problems with well over billions of examples.

Boosting refers to any Ensemble method that can combine several weak learners into a strong learner. The general idea of most boosting methods is to train predictors sequentially, each trying to correct its predecessor. There are many boosting methods available, one of the most popular is AdaBoost (Adaptive Boosting). The way for a new predictor to correct its predecessor is to pay a bit more attention to the training instances that the predecessor underfitted. This is the technique used by AdaBoost.

We report two essential improvements in readability assessment: 1. three novel features in advanced semantics and 2. the timely evidence that traditional ML models (e.g. Random Forest, using handcrafted features) can combine with transformers (e.g. RoBERTa) to augment model performance. First, we explore suitable transformers and traditional ML models. Then, we extract 255 handcrafted linguistic features using self-developed extraction software. Finally, we assemble those to create several hybrid models, achieving state-of-the-art (SOTA) accuracy on popular datasets in readability assessment. The use of handcrafted features help model performance on smaller datasets. Notably, our RoBERTA-RF-T1 hybrid achieves the near-perfect classification accuracy of 99%, a 20.3% increase from the previous SOTA.

In this chapter the programs fit_func_miso.py and fit_func_mimo.py are presented and they are technically wrappers of the class XGBRFRegressor of the XGBoost library and which purpose is to allow the use of the regression of the underlying regressor to fit functions without having to write code but only acting on the command line. In fact through the argument --xgbparams the user passes a series of hyper-parameters to adjust the behavior of the underlying XGBoost regressor algorithm and others to configure its learning phase. In addition to the parameters of the underlying regressor the two programs support their own arguments to allow the user to pass the training dataset and optionally the validation dataset, on which file to save the trained model, the metrics to calculate during the training, constraints for regularization (e.g. The program fit_func_miso.py, as well as the underlying XGBoost regressor, is of type M.I.S.O., i.e. Multiple Input Single Output: it is designed to fit a function of the form $f \colon \rm I\!R n \to \rm I\!R$ where the number of independent variables is arbitrarily large while the output dependent variable is only one.

In [12], Mourtada, Ga\"{i}ffas and Scornet showed that, under proper tuning of the complexity parameters, random trees and forests built from the Mondrian process in $\mathbb{R}^d$ achieve the minimax rate for $\beta$-H\"{o}lder continuous functions, and random forests achieve the minimax rate for $(1+\beta)$-H\"{o}lder functions in arbitrary dimension. In this work, we show that a much larger class of random forests built from random partitions of $\mathbb{R}^d$ also achieve these minimax rates. This class includes STIT random forests, the most general class of random forests built from a self-similar and stationary partition of $\mathbb{R}^d$ by hyperplane cuts possible, as well as forests derived from Poisson hyperplane tessellations. Our proof technique relies on classical results as well as recent advances on stationary random tessellations in stochastic geometry.

Local decision rules are commonly understood to be more explainable, due to the local nature of the patterns involved. With numerical optimization methods such as gradient boosting, ensembles of local decision rules can gain good predictive performance on data involving global structure. Meanwhile, machine learning models are being increasingly used to solve problems in high-stake domains including healthcare and finance. Here, there is an emerging consensus regarding the need for practitioners to understand whether and how those models could perform robustly in the deployment environments, in the presence of distributional shifts. Past research on local decision rules has focused mainly on maximizing discriminant patterns, without due consideration of robustness against distributional shifts. In order to fill this gap, we propose a new method to learn and ensemble local decision rules, that are robust both in the training and deployment environments. Specifically, we propose to leverage causal knowledge by regarding the distributional shifts in subpopulations and deployment environments as the results of interventions on the underlying system. We propose two regularization terms based on causal knowledge to search for optimal and stable rules. Experiments on both synthetic and benchmark datasets show that our method is effective and robust against distributional shifts in multiple environments.