Often, relations between economic variables cannot be exploited for forecasting, suggesting that predictors are weak in the sense estimation uncertainty is larger than bias from ignoring relation. In this paper, we propose a novel bagging estimator designed such predictors. Based on test finite-sample predictive ability, our shrinks ordinary least squares estimate—not to zero, but towards null ...

We present a visual tablet for exploring the nature of a bagged decision tree (Breiman [1996]). Aggregating classifiers over bootstrap datasets (bagging) can result in greatly improved prediction accuracy. Bagging is motivated as a variance reduction technique, but it is considered a black box with respect to interpretation. Current research seekine: to explain why bagging works has focused ond...

Bagging is a useful method to improve fruit quality by altering its exposure to light, whereas its effect on fruit volatiles production is inconsistent, and the genes responsible for the observed changes remain unknown. In the present study, single-layer yellow paper bags were used to study the effects of bagging treatment on the formation of C6 aldehydes in peach fruit (Prunus persica L. Batsc...

Ensembles of decision trees often exhibit greater predictive accuracy than single trees alone. Bagging and boosting are two standard ways of generating and combining multiple trees. Boosting has been empirically determined to be the more eeective of the two, and it has recently been proposed that this may be because it produces more diverse trees than bagging. This paper reports empirical nding...

Learning from imbalanced data is an important problem in data mining research. Much research has addressed the problem of imbalanced data by using sampling methods to generate an equally balanced training set to improve the performance of the prediction models, but it is unclear what ratio of class distribution is best for training a prediction model. Bagging is one of the most popular and effe...

Bagging and boosting are among the most popular resampling ensemble methods that generate and combine a diversity of classifiers using the same learning algorithm for the base-classifiers. Boosting algorithms are considered stronger than bagging on noisefree data. However, there are strong empirical indications that bagging is much more robust than boosting in noisy settings. For this reason, i...

The error rate of decision-tree and other classi-cation learners can often be much reduced by bagging: learning multiple models from bootstrap samples of the database, and combining them by uniform voting. In this paper we empirically test two alternative explanations for this, both based on Bayesian learning theory: (1) bagging works because it is an approximation to the optimal procedure of B...

Many applications aim to learn a high dimensional parameter of a data generating distribution based on a sample of independent and identically distributed observations. For example, the goal might be to estimate the conditional mean of an outcome given a list of input variables. In this prediction context, bootstrap aggregating (bagging) has been introduced as a method to reduce the variance of...

Abstract: Bagging is a device intended for reducing the prediction error of learning algorithms. In its simplest form, bagging draws bootstrap samples from the training sample, applies the learning algorithm to each bootstrap sample, and then averages the resulting prediction rules. More generally, the resample size M may be different from the original sample size N , and resampling can be done...