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Bootstrap aggregating ( bagging ) is a machine learning ensemble meta-algorithm to improve machine learning of statistical classification and regression models in terms of stability and classification accuracy. It also reduces variance and helps to avoid overfitting . Although it is usually applied to decision tree models, it can be used with any type of model. Bagging is a special case of the model averaging approach. Since the method averages several predictors, it is not useful for improving linear models. Similarly, bagging does not improve very stable models like k nearest neighbors.
Bootstrap aggregating - Wikipedia, the free encyclopedia
Knowledge market - Wikipedia, the free encyclopedia
A knowledge market is a mechanism for distributing knowledge resources. There are two views on knowledge and how knowledge markets can function. One view uses a legal construct of intellectual property to make knowledge a typical scarce resource, so the traditional commodity market mechanism can be applied directly to distribute it.
Result of particle filtering (red line) based on observed data generated from the blue line ( Much larger image ) In statistics , particle filters , also known as Sequential Monte Carlo methods (SMC), are sophisticated model estimation techniques based on simulation . [ 1 ] Particle filters have important applications in econometrics , [ 2 ] and in other fields. Particle filters are usually used to estimate Bayesian models in which the latent variables are connected in a Markov chain — similar to a hidden Markov model (HMM), but typically where the state space of the latent variables is continuous rather than discrete, and not sufficiently restricted to make exact inference tractable (as, for example, in a linear dynamical system , where the state space of the latent variables is restricted to Gaussian distributions and hence exact inference can be done efficiently using a Kalman filter ).
Particle filter - Wikipedia, the free encyclopedia
In statistics , importance sampling is a general technique for estimating properties of a particular distribution , while only having samples generated from a different distribution rather than the distribution of interest. It is related to Umbrella sampling in computational physics . Depending on the application, the term may refer to the process of sampling from this alternative distribution, the process of inference, or both. [ edit ] Basic theory More formally, let be a random variable in some probability space .
Importance sampling - Wikipedia, the free encyclopedia
Resampling (statistics) - Wikipedia, the free encyclopedia
In statistics , resampling is any of a variety of methods for doing one of the following: Estimating the precision of sample statistics ( medians , variances , percentiles ) by using subsets of available data ( jackknifing ) or drawing randomly with replacement from a set of data points ( bootstrapping ) Exchanging labels on data points when performing significance tests ( permutation tests , also called exact tests , randomization tests, or re-randomization tests) Bootstrapping is a statistical method for estimating the sampling distribution of an estimator by sampling with replacement from the original sample, most often with the purpose of deriving robust estimates of standard errors and confidence intervals of a population parameter like a mean , median , proportion , odds ratio , correlation coefficient or regression coefficient. It may also be used for constructing hypothesis tests.