We develop a Random Forest model to estimate the species distribution of Asian elephants in India and study the inter and intra-annual spatiotemporal variability of habitats suitable for them. Climatic, topographic variables and satellite-derived Land Use/Land Cover (LULC), Net Primary Productivity (NPP), Leaf Area Index (LAI), and Normalized Difference Vegetation Index (NDVI) are used as predictors, and the species sighting data of Asian elephants from Global Biodiversity Information Reserve is used to develop the Random Forest model. A careful hyper-parameter tuning and training-validation-testing cycle are completed to identify the significant predictors and develop a final model that gives precision and recall of 0.78 and 0.77. The model is applied to estimate the spatial and temporal variability of suitable habitats. We observe that seasonal reduction in the suitable habitat may explain the migration patterns of Asian elephants and the increasing human-elephant conflict. Further, the total available suitable habitat area is observed to have reduced, which exacerbates the problem. This machine learning model is intended to serve as an input to the Agent-Based Model that we are building as part of our Artificial Intelligence-driven decision support tool to reduce human-wildlife conflict.

Machine learning is a branch of computer science that has the potential to transform epidemiologic sciences. Amid a growing focus on "Big Data," it offers epidemiologists new tools to tackle problems for which classical methods are not well-suited. In order to critically evaluate the value of integrating machine learning algorithms and existing methods, however, it is essential to address language and technical barriers between the two fields that can make it difficult for epidemiologists to read and assess machine learning studies. Here, we provide an overview of the concepts and terminology used in machine learning literature, which encompasses a diverse set of tools with goals ranging from prediction to classification to clustering. We provide a brief introduction to 5 common machine learning algorithms and 4 ensemble-based approaches. We then summarize epidemiologic applications of machine learning techniques in the published literature. We recommend approaches to incorporate machine learning in epidemiologic research and discuss opportunities and challenges for integrating machine learning and existing epidemiologic research methods. Machine learning is a branch of computer science that broadly aims to enable computers to "learn" without being directly programmed (1). It has origins in the artificial intelligence movement of the 1950s and emphasizes practical objectives and applications, particularly prediction and optimization. Computers "learn" in machine learning by improving their performance at tasks through "experience" (2, p. xv). In practice, "experience" usually means fitting to data; hence, there is not a clear boundary between machine learning and statistical approaches. Indeed, whether a given methodology is considered "machine learning" or "statistical" often reflects its history as much as genuine differences, and many algorithms (e.g., least absolute shrinkage and selection operator (LASSO), stepwise regression) may or may not be considered machine learning depending on who you ask. Still, despite methodological similarities, machine learning is philosophically and practically distinguishable. At the liberty of (considerable) oversimplification, machine learning generally emphasizes predictive accuracy over hypothesis-driven inference, usually focusing on large, high-dimensional (i.e., having many covariates) data sets (3, 4). Regardless of the precise distinction between approaches, in practice, machine learning offers epidemiologists important tools. In particular, a growing focus on "Big Data" emphasizes problems and data sets for which machine learning algorithms excel while more commonly used statistical approaches struggle. This primer provides a basic introduction to machine learning with the aim of providing readers a foundation for critically reading studies based on these methods and a jumping-off point for those interested in using machine learning techniques in epidemiologic research.

Decision Tree Algorithm one of the easiest and popular Algorithms to predict the output. The Decision Tree Algorithm is a part of the supervised machine learning algorithm. Here the problem is represented in a form of a tree to predict the outcome. This algorithm aims to create a model that should predict the value of a variable that is targeted, and for this purpose, it is represented in a form of a decision tree. It is used for classification problems and also for regression problems.

In a previous post I briefly touched on the problem with overfitting, which is loosely defined as a machine learning model that memorizes a training data set and thus provides high accuracy for predictions using it, but then performs poorly when presented with new data -- a phenomenon known as variance. The post discussed the Random Forest approach using bootstrap aggregation to address this issue, but it begged the question: "Why does intentionally producing lower-quality data sets and averaging across their results produce better predictions?" Reality, it turns out, is messy, so intentionally introducing inaccuracy in the process of producing predictions (that's some impressive alliteration, don't you think?) usually makes them better. It's a process known as regularization. It turns out that all kinds of machine learning algorithms have overfitting risks, and they way you regularize depends on the model you're trying to fit.

This article will cover one of the most advanced algorithms and most widely used in analytical applications. This is an extensive subject, as we have several algorithms and various techniques for working with decision trees. On the other hand, these algorithms are among the most powerful in Machine Learning and are easy to interpret. So, let's start by defining what decision trees are and their representation through machine learning algorithms. For decision tree learning models, we will study some algorithms with C4.5, C5.0, CART, and ID3.

ABSTRACT Typical state of the art flow cytometry data samples consists of measures of more than 100.000 cells in 10 or more features. AI systems are able to diagnose such data with almost the same accuracy as human experts. However, there is one central challenge in such systems: their decisions have far-reaching consequences for the health and life of people, and therefore, the decisions of AI systems need to be understandable and justifiable by humans. In this work, we present a novel explainable AI method, called ALPODS, which is able to classify (diagnose) cases based on clusters, i.e., subpopulations, in the high-dimensional data. ALPODS is able to explain its decisions in a form that is understandable for human experts. For the identified subpopulations, fuzzy reasoning rules expressed in the typical language of domain experts are generated. A visualization method based on these rules allows human experts to understand the reasoning used by the AI system. A comparison to a selection of state of the art explainable AI systems shows that ALPODS operates efficiently on known benchmark data and also on everyday routine case data. KEYWORDS: Explainable AI, Expert System, Symbolic System, Biomedical Data 1. INTRODUCTION State of the art machine learning (ML) artificial intelligence (AI) algorithms are effectively and efficiently able to diagnose (classify) high-dimensional data sets in modern medicine, e.g., for multiparameter flow cytometry data [Hu et al., 2019; Zhao et al., 2020]. These are systems that, after a training (learning) phase using learning data, perform well on data that are not part of the training data, i.e., the test data. This is called supervised learning [Murphy, 2012].

The common belief about the growing medium of livestreaming is that its value lies in its "live" component. In this paper, we leverage data from a large livestreaming platform to examine this belief. We are able to do this as this platform also allows viewers to purchase the recorded version of the livestream. We summarize the value of livestreaming content by estimating how demand responds to price before, on the day of, and after the livestream. We do this by proposing a generalized Orthogonal Random Forest framework. This framework allows us to estimate heterogeneous treatment effects in the presence of high-dimensional confounders whose relationships with the treatment policy (i.e., price) are complex but partially known. We find significant dynamics in the price elasticity of demand over the temporal distance to the scheduled livestreaming day and after. Specifically, demand gradually becomes less price sensitive over time to the livestreaming day and is inelastic on the livestreaming day. Over the post-livestream period, demand is still sensitive to price, but much less than the pre-livestream period. This indicates that the vlaue of livestreaming persists beyond the live component. Finally, we provide suggestive evidence for the likely mechanisms driving our results. These are quality uncertainty reduction for the patterns pre- and post-livestream and the potential of real-time interaction with the creator on the day of the livestream.

It is an important management task of metro agencies to formulate reasonable improvement schemes based on the result of service quality surveys. Considering scores, weights, and improvement feasibility of service quality attributes in a certain period, this paper integrates Decision Tree (DT) into Importance-Performance analysis (IPA) to build a DT-IPA model, which is used to determine the improvement priority of attributes, and to quantify the improvement degree. If-then rules extracted from the optimal decision tree and the improvement feasibility computed by analytic hierarchy process are two main items derived from the DT-IPA model. They are used to optimize the initial improvement priority of attributes determined by IPA and to quantify the degree of improvement of the adjusted attributes. Then, the overall service quality can reach a high score, realizing the operation goal. The effectiveness of the DT-IPA model was verified through an empirical study which was taken place in Changsha Metro, China. The proposed method can be a decision-making tool for metro agency managers to improve the quality of metro service.

Greedy decision tree learning heuristics are mainstays of machine learning practice, but theoretical justification for their empirical success remains elusive. In fact, it has long been known that there are simple target functions for which they fail badly (Kearns and Mansour, STOC 1996). Recent work of Brutzkus, Daniely, and Malach (COLT 2020) considered the smoothed analysis model as a possible avenue towards resolving this disconnect. Within the smoothed setting and for targets $f$ that are $k$-juntas, they showed that these heuristics successfully learn $f$ with depth-$k$ decision tree hypotheses. They conjectured that the same guarantee holds more generally for targets that are depth-$k$ decision trees. We provide a counterexample to this conjecture: we construct targets that are depth-$k$ decision trees and show that even in the smoothed setting, these heuristics build trees of depth $2^{\Omega(k)}$ before achieving high accuracy. We also show that the guarantees of Brutzkus et al. cannot extend to the agnostic setting: there are targets that are very close to $k$-juntas, for which these heuristics build trees of depth $2^{\Omega(k)}$ before achieving high accuracy.

In recent years, machine learning has begun automating decision making in fields as varied as college admissions, credit lending, and criminal sentencing. The socially sensitive nature of some of these applications together with increasing regulatory constraints has necessitated the need for algorithms that are both fair and interpretable. In this paper we consider the problem of building Boolean rule sets in disjunctive normal form (DNF), an interpretable model for binary classification, subject to fairness constraints. We formulate the problem as an integer program that maximizes classification accuracy with explicit constraints on two different measures of classification parity: equality of opportunity and equalized odds. Column generation framework, with a novel formulation, is used to efficiently search over exponentially many possible rules. When combined with faster heuristics, our method can deal with large data-sets. Compared to other fair and interpretable classifiers, our method is able to find rule sets that meet stricter notions of fairness with a modest trade-off in accuracy.