"They don't want to leave one out over the dish," Alonso said of the Yankees.
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"The third-millennium heart is a / place of many chambers," is how the book begins, and I had to wonder whether a translation could really recreate every chamber, or whether it would have to leave one out.
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Leave-one-out cross- validation is asymptotically equivalent to AIC, for ordinary linear regression models. Asymptotic equivalence to AIC also holds for mixed-effects models.
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In leave-one-out cross-validation, for each sequence in the data set, the algorithm is run on the rest of the data set to select a minimum set of tagging SNPs.
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The stability of an algorithm is a property of the learning process, rather than a direct property of the hypothesis space H, and it can be assessed in algorithms that have hypothesis spaces with unbounded or undefined VC-dimension such as nearest neighbor. A stable learning algorithm is one for which the learned function does not change much when the training set is slightly modified, for instance by leaving out an example. A measure of Leave one out error is used in a Cross Validation Leave One Out (CVloo) algorithm to evaluate a learning algorithm's stability with respect to the loss function. As such, stability analysis is the application of sensitivity analysis to machine learning.
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Thus the GCV formula adjusts (i.e. increases) the training RSS to take into account the flexibility of the model. We penalize flexibility because models that are too flexible will model the specific realization of noise in the data instead of just the systematic structure of the data. Generalized cross-validation is so named because it uses a formula to approximate the error that would be determined by leave-one- out validation.
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Many people have helped me to complete this one, sometimes without even knowing it. They are so numerous that I will not even attempt to acknowledge them individually, for fear that I might leave one out."It Takes a Village, p. 319. During her promotional tour for the book, Clinton said, "I actually wrote the book ... I had to write my own book because I want to stand by every word.
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For example, setting k = 2 results in 2-fold cross-validation. In 2-fold cross-validation, we randomly shuffle the dataset into two sets d0 and d1, so that both sets are equal size (this is usually implemented by shuffling the data array and then splitting it in two). We then train on d0 and validate on d1, followed by training on d1 and validating on d0. When k = n (the number of observations), k-fold cross-validation is equivalent to leave-one-out cross-validation.
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Treatment effect or radiation necrosis after stereotactic radiosurgery (SRS) for brain metastases is a common phenomenon often indistinguishable from true progression. Radiomics demonstrated significant differences in a set of 82 treated lesions in 66 patients with pathological outcomes. Top-ranked Radiomic features feed into an optimized IsoSVM classifier resulted in a sensitivity and specificity of 65.38% and 86.67%, respectively, with an area under the curve of 0.81 on leave-one-out cross-validation. Only 73% of cases were classifiable by the neuroradiologist, with a sensitivity of 97% and specificity of 19%.
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For example, leave one-out cross- validation generally leads to an overestimation of predictive capacity. Even with external validation, it is difficult to determine whether the selection of training and test sets was manipulated to maximize the predictive capacity of the model being published. Different aspects of validation of QSAR models that need attention include methods of selection of training set compounds, setting training set size and impact of variable selection for training set models for determining the quality of prediction. Development of novel validation parameters for judging quality of QSAR models is also important.
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The Clayton's diversity measure can be used to define how well a set of tag SNPs differentiate different haplotypes. This measure is suitable only for haplotype blocks with limited haplotype diversity and it is not clear how to use it for large data sets consisting of multiple haplotype blocks. Some recent works evaluate tag SNPs selection algorithms based on how well the tagging SNPs can be used to predict non-tagging SNPs. The prediction accuracy is determined using cross-validation such as leave- one-out or hold out.
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Consider predicting the class label of a single data point by consensus of its k-nearest neighbours with a given distance metric. This is known as leave-one-out classification. However, the set of nearest-neighbours C_i can be quite different after passing all the points through a linear transformation. Specifically, the set of neighbours for a point can undergo discrete changes in response to smooth changes in the elements of A, implying that any objective function f(\cdot) based on the neighbours of a point will be piecewise-constant, and hence not differentiable.
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The meta-analysis estimate represents a weighted average across studies and when there is heterogeneity this may result in the summary estimate not being representative of individual studies. Qualitative appraisal of the primary studies using established tools can uncover potential biases, but does not quantify the aggregate effect of these biases on the summary estimate. Although the meta-analysis result could be compared with an independent prospective primary study, such external validation is often impractical. This has led to the development of methods that exploit a form of leave-one-out cross validation, sometimes referred to as internal-external cross validation (IOCV).
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Neighbourhood components analysis aims at "learning" a distance metric by finding a linear transformation of input data such that the average leave-one-out (LOO) classification performance is maximized in the transformed space. The key insight to the algorithm is that a matrix A corresponding to the transformation can be found by defining a differentiable objective function for A, followed by use of an iterative solver such as conjugate gradient descent. One of the benefits of this algorithm is that the number of classes k can be determined as a function of A, up to a scalar constant. This use of the algorithm therefore addresses the issue of model selection.
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Jackknifing and bootstrapping, well-known statistical resampling procedures, have been employed with parsimony analysis. The jackknife, which involves resampling without replacement ("leave-one-out") can be employed on characters or taxa; interpretation may become complicated in the latter case, because the variable of interest is the tree, and comparison of trees with different taxa is not straightforward. The bootstrap, resampling with replacement (sample x items randomly out of a sample of size x, but items can be picked multiple times), is only used on characters, because adding duplicate taxa does not change the result of a parsimony analysis. The bootstrap is much more commonly employed in phylogenetics (as elsewhere); both methods involve an arbitrary but large number of repeated iterations involving perturbation of the original data followed by analysis.
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