An Introduction to Statistical Learning

James, Gareth; Witten, Daniela; Hastie, Trevor; Tibshirani, Robert

doi:10.1007/978-1-4614-7138-7

Cited by 8,257 publications

(6,791 citation statements)

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“…a hold-out group) while the model is fit to the remaining folds similar to LOOCV [63]. All statistical analyses were performed with R version 3.0.1 [64].…”

Section: Statistical Analysesmentioning

confidence: 99%

Population differences in the postcrania of modern South Africans and the implications for ancestry estimation

Liebenberg

L'Abbé

Stull

2015

Forensic Science International

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Highlights AbstractThe cranium is widely recognized as the most important skeletal element to use when evaluating population differences and estimating ancestry. However, the cranium is not always intact or available for analysis, which emphasizes the need for postcranial alternatives. The purpose of this study was to quantify postcraniometric differences among South Africans that can be used to estimate ancestry.Thirty-nine standard measurements from 11 postcranial bones were collected from 360 modern black, white and coloured South Africans; the sex and ancestry distribution were equal. Group differences were explored with analysis of variance (ANOVA) and Tukey's honestly significant difference (HSD) test. Linear and flexible discriminant analysis (LDA and FDA, respectively) were conducted with bone 2 models as well as numerous multivariate subsets to identify the model and method that yielded the highest correct classifications. Leave-one-out (LDA) and k-fold (k = 10; FDA) cross-validation with equal priors were used for all models. ANOVA and Tukey's HSD results reveal statistically significant differences between at least two of the three groups for the majority of the variables, with varying degrees of group overlap. Bone models, which consisted of all measurements per bone, resulted in low accuracies that ranged from 46% to 63% (LDA) and 41% to 66% (FDA). In contrast, the multivariate subsets, which consisted of different variable combinations from all elements, achieved accuracies as high as 85% (LDA) and 87% (FDA). Thus, when using a multivariate approach, the postcranial skeleton can distinguish among three modern South African groups with high accuracy.

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“…a hold-out group) while the model is fit to the remaining folds similar to LOOCV [63]. All statistical analyses were performed with R version 3.0.1 [64].…”

Section: Statistical Analysesmentioning

confidence: 99%

Population differences in the postcrania of modern South Africans and the implications for ancestry estimation

Liebenberg

L'Abbé

Stull

2015

Forensic Science International

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“…In general terms, supervised statistical learning is employed as a statistical model to estimate or predict an output using relevant inputs in various areas such as public policy, medicine, astrophysics and business. In unsupervised statistical learning, learning of relationships and structure of data is possible without supervising the output [5]. In this The Naive Bayes method is applied to learn and represent probabilistic information from data with clear and easy understanding by using supervised learning tasks in which classes are known in training phase, in which prediction of classes is realized in the test phase [20].…”

Section: Overview Of Statistical Learningmentioning

confidence: 99%

“…Statistical learning refers to a set of tools for modelling and understanding complex datasets. It is a recently developed area in statistics and blends with parallel developments in computer science and, in particular, machine learning [5].…”

Section: Introductionmentioning

confidence: 99%

The Usage of Statistical Learning Methods on Wearable Devices and a Case Study: Activity Recognition on Smartwatches

Ballı¹,

Sağbaş²

2017

Advances in Statistical Methodologies and Their Application to Real Problems

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The aim of this study is to explore the usage of statistical learning methods on wearable devices and realize an experimental study for recognition of human activities by using smartwatch sensor data. To achieve this objective, mobile applications that run on smartwatch and smartphone were developed to gain training data and detect human activity momentarily; 500 pattern data were obtained with 4-second intervals for each activity (walking, typing, stationary, running, standing, writing on board, brushing teeth, cleaning and writing). Created dataset was tested with five different statistical learning methods (Naive Bayes, k nearest neighbour (kNN), logistic regression, Bayesian network and multilayer perceptron) and their performances were compared.

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“…In assessing statistical medical data it is recommended to consider a prevalence rate for particular location [30]. For this purpose β 0 is transformed in β 0 n…”

Section: Data Processingmentioning

confidence: 99%