14,129 research outputs found

    An Introduction to Recursive Partitioning: Rationale, Application and Characteristics of Classification and Regression Trees, Bagging and Random Forests

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    Recursive partitioning methods have become popular and widely used tools for nonparametric regression and classification in many scientific fields. Especially random forests, that can deal with large numbers of predictor variables even in the presence of complex interactions, have been applied successfully in genetics, clinical medicine and bioinformatics within the past few years. High dimensional problems are common not only in genetics, but also in some areas of psychological research, where only few subjects can be measured due to time or cost constraints, yet a large amount of data is generated for each subject. Random forests have been shown to achieve a high prediction accuracy in such applications, and provide descriptive variable importance measures reflecting the impact of each variable in both main effects and interactions. The aim of this work is to introduce the principles of the standard recursive partitioning methods as well as recent methodological improvements, to illustrate their usage for low and high dimensional data exploration, but also to point out limitations of the methods and potential pitfalls in their practical application. Application of the methods is illustrated using freely available implementations in the R system for statistical computing

    Effects of inflation expectations on macroeconomic dynamics: Extrapolative versus regressive expectations

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    In this paper we integrate heterogeneous inflation expectations into a simple monetary model. Guided by empirical evidence we assume that boundedly rational agents, selecting between extrapolative and regressive forecasting rules to predict the future inflation rate, prefer rules that have produced low prediction errors in the past. We show that integrating this behavioral expectation formation process into the monetary model leads to the possibility of endogenous macroeconomic dynamics. For instance, our model replicates certain empirical regularities such as irregular growth cycles or inflation persistence. Moreover, we observe multi-stability via a Chenciner bifurcation. --Extrapolative and regressive expectations,dynamic predictor selection,macroeconomic dynamics,nonlinearities and chaos,bifurcation analysis

    Animal spirits and credit cycles

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    In this paper we extend the behavioral macroeconomic model as proposed by De Grauwe (2012) to include a banking sector. The behavioral model takes the view that agents have limited cognitive abilities. As a result, it is “rational” to use simple forecasting rules and to subject the use of these rules to a fitness test. Agents are then driven to select the rule that performs best. The behavioral model produces endogenous and self-fulfilling movements of optimism and pessimism (animal spirits). Our main result is that the existence of banks intensifies these movements, creating a greater scope for booms and busts. Thus, banks do not create but amplify animal spirits. We find that increases in the equity ratios of banks tend to reduce the importance of animal spirits over the business cycle. The other policy conclusion we derive from our results is that the central bank has an important responsibility for stabilising output: output stabilization is an instrument to “tame the animal spirits”. This has the effect of improving the trade-off between inflation and output volatility

    Bayesian learning of models for estimating uncertainty in alert systems: application to air traffic conflict avoidance

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    Alert systems detect critical events which can happen in the short term. Uncertainties in data and in the models used for detection cause alert errors. In the case of air traffic control systems such as Short-Term Conflict Alert (STCA), uncertainty increases errors in alerts of separation loss. Statistical methods that are based on analytical assumptions can provide biased estimates of uncertainties. More accurate analysis can be achieved by using Bayesian Model Averaging, which provides estimates of the posterior probability distribution of a prediction. We propose a new approach to estimate the prediction uncertainty, which is based on observations that the uncertainty can be quantified by variance of predicted outcomes. In our approach, predictions for which variances of posterior probabilities are above a given threshold are assigned to be uncertain. To verify our approach we calculate a probability of alert based on the extrapolation of closest point of approach. Using Heathrow airport flight data we found that alerts are often generated under different conditions, variations in which lead to alert detection errors. Achieving 82.1% accuracy of modelling the STCA system, which is a necessary condition for evaluating the uncertainty in prediction, we found that the proposed method is capable of reducing the uncertain component. Comparison with a bootstrap aggregation method has demonstrated a significant reduction of uncertainty in predictions. Realistic estimates of uncertainties will open up new approaches to improving the performance of alert systems

    Tree Boosting Data Competitions with XGBoost

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    This Master's Degree Thesis objective is to provide understanding on how to approach a supervised learning predictive problem and illustrate it using a statistical/machine learning algorithm, Tree Boosting. A review of tree methodology is introduced in order to understand its evolution, since Classification and Regression Trees, followed by Bagging, Random Forest and, nowadays, Tree Boosting. The methodology is explained following the XGBoost implementation, which achieved state-of-the-art results in several data competitions. A framework for applied predictive modelling is explained with its proper concepts: objective function, regularization term, overfitting, hyperparameter tuning, k-fold cross validation and feature engineering. All these concepts are illustrated with a real dataset of videogame churn; used in a datathon competition

    A Survey on Compiler Autotuning using Machine Learning

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    Since the mid-1990s, researchers have been trying to use machine-learning based approaches to solve a number of different compiler optimization problems. These techniques primarily enhance the quality of the obtained results and, more importantly, make it feasible to tackle two main compiler optimization problems: optimization selection (choosing which optimizations to apply) and phase-ordering (choosing the order of applying optimizations). The compiler optimization space continues to grow due to the advancement of applications, increasing number of compiler optimizations, and new target architectures. Generic optimization passes in compilers cannot fully leverage newly introduced optimizations and, therefore, cannot keep up with the pace of increasing options. This survey summarizes and classifies the recent advances in using machine learning for the compiler optimization field, particularly on the two major problems of (1) selecting the best optimizations and (2) the phase-ordering of optimizations. The survey highlights the approaches taken so far, the obtained results, the fine-grain classification among different approaches and finally, the influential papers of the field.Comment: version 5.0 (updated on September 2018)- Preprint Version For our Accepted Journal @ ACM CSUR 2018 (42 pages) - This survey will be updated quarterly here (Send me your new published papers to be added in the subsequent version) History: Received November 2016; Revised August 2017; Revised February 2018; Accepted March 2018
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