variance function

Original: http://www.zhihu.com/question/27068705What are the differences and linkages between bias (deviations), error (Error), and variance (variance) in machine learning? Modification recently in Learning machine learning, learning to

First, the basic principles of statistics12 Sample t Test Condition: ① Both obey normal distribution, ② two population variance is equal, namely variance homogeneity.2 pairs of T-Test conditions: The difference of the overall normal distribution can

This article corresponds to "r language combat" the 9th chapter: Variance analysis; Chapter 10th: Efficacy Analysis====================================================================Variance Analysis:Regression analysis is to predict the quantified

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Bias and varianceReference: http://scott.fortmann-roe.com/docs/BiasVariance.htmlhttp://www.cnblogs.com/kemaswill/Bias-variance decomposition is an important analytical technique in machine learning. Given the learning goal and the training set scale,

1.1 Hotelling T2 TestHotelling T2 test is a common multivariate testing method, which is a natural generalization of single-variable test, and is often used for the comparison of two groups of mean vectors.A sample of two content analysis is n,m

The address of this article is http://www.cnblogs.com/kemaswill/. the contact person is kemaswill@163.com. The goal of machine learning is to learn a model with better generalization ability. The so-called generalization ability refers to the

I found that this Shadow technology has almost no detailed explanation in the Chinese version.     Variance shadow maps William DonnellyAndrew lauritzen WaterlooUniversity, computer science, graphics lab Winger_wTranslation Limited

# -- * -- Coding: UTF-8 -- * -- import mathimport itertoolsdef mean (t): "mean" Return float (sum (t)/Len (t) def E (X, p): "discretization mathematical expectation (also called mean) of a random variable ): the sum of the product of random variable

Drawing a learning curve is useful, for example, if you want to check your learning algorithm and run normally. Or you want to improve the performance or effect of the algorithm. Then the learning curve is a good tool. The learning curve can judge a