DATA SCIENCE QUESTIONS AND ANSWERS
Bootstrapping. In statistics, bootstrapping is any test or metric that relies on random sampling with replacement. Bootstrapping allows assigning measures of accuracy (defined in terms of bias, variance, confidence intervals, prediction error or some other such measure) to sample estimates. This technique allows estimation of the sampling distribution of almost any statistic using random sampling methods. Generally, it falls in … Read more
Boltzmann machine is a network of symmetrically connected, neuronlike units that make stochastic decisions about whether to be on or off. Boltzmann machines have a simple learning algorithm that allows them to discover interesting features in datasets composed of binary vectors. The learning algorithm is very slow in networks with many layers of feature detectors, but it can be made … Read more
Big data is a term for data sets that are so large or complex that traditional data processing application software is inadequate to deal with them. Challenges include capture, storage, analysis, data curation, search, sharing, transfer, visualization, querying, updating and information privacy. The term “big data” often refers simply to the use of predictive analytics, user behavior analytics, or certain … Read more
Bias-variance trade-off is a central problem in supervised learning. Ideally, one wants to choose a model that both accurately captures the regularities in its training data, but also generalizes well to unseen data. In statistics and machine learning bias-variance trade-off is the problem of simultaneously minimizing two sources of error that prevent supervised learning algorithms from generalizing beyond their training … Read more
Bayesian statistics is a theory in the field of statistics in which the evidence about the true state of the world is expressed in terms of degrees of belief known as Bayesian probabilities. Such an interpretation is only one of a number of interpretations of probability and there are other statistical techniques that are not based on ‘degrees of belief’. … Read more
Backpropagation or the backward propagation of errors is a common method of training artificial neural networks and used in conjunction with an optimization method such as gradient descent. The algorithm repeats a two-phase cycle, propagation, and weight update. When an input vector is presented to the network, it is propagated forward through the network, layer by layer, until it reaches … Read more
Autoencoder is an artificial neural network used for unsupervised learning of efficient codings. The aim of an autoencoder is to learn a representation (encoding) for a set of data, typically for the purpose of dimensionality reduction. Recently, the autoencoder concept has become more widely used for learning generative models of data. the simplest form of an autoencoder is a feedforward, … Read more
AUC stands for the Area Under the Curve. Technically, it can be used for the area under any number of curves that are used to measure the performance of a model, for example, it could be used for the area under a precision-recall curve. However, when not otherwise specified, AUC is almost always taken to mean the area under the … Read more
Anova F-test in a one-way analysis of variance is used to assess whether the expected values of a quantitative variable within several pre-defined groups differ from each other. For example, suppose that a medical trial compares four treatments. The ANOVA F-test can be used to assess whether any of the treatments is on average superior, or inferior, to the others … Read more
ANOVA -Analysis of variance is a form of statistical hypothesis testing used in the analysis of experimental data. A test result is called statistically significant if it is deemed unlikely to have occurred by chance, assuming the truth of the null hypothesis. A statistically significant result, when a probability (p-value) is less than a threshold (significance level), justifies the rejection … Read more