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43 relations: Annals of Statistics, Bias–variance tradeoff, Boosting (machine learning), Bootstrap aggregating, Correlation and dependence, Cross-validation (statistics), Decision tree, Decision tree learning, Donald Geman, Ensemble learning, Feature (machine learning), Generalization error, Gradient boosting, Independent and identically distributed random variables, K-nearest neighbors algorithm, Kernel method, Kullback–Leibler divergence, Lecture Notes in Computer Science, Leo Breiman, Linear subspace, Lipschitz, Machine Learning (journal), Mode (statistics), Multinomial logistic regression, Naive Bayes classifier, Neural Computation (journal), Nonparametric statistics, Orange (software), Out-of-bag error, Overfitting, Partial permutation, R (programming language), Random forest, Random subspace method, Randomized algorithm, Regression analysis, Scikit-learn, Statistical classification, Tin Kam Ho, Trademark, Training, test, and validation sets, Trevor Hastie, Wikipedia.
Annals of Statistics
The Annals of Statistics is a peer-reviewed statistics journal published by the Institute of Mathematical Statistics.
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Bias–variance tradeoff
In statistics and machine learning, the bias–variance tradeoff is the property of a set of predictive models whereby models with a lower bias in parameter estimation have a higher variance of the parameter estimates across samples, and vice versa.
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Boosting (machine learning)
Boosting is a machine learning ensemble meta-algorithm for primarily reducing bias, and also variance in supervised learning, and a family of machine learning algorithms that convert weak learners to strong ones.
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Bootstrap aggregating
Bootstrap aggregating, also called bagging, is a machine learning ensemble meta-algorithm designed to improve the stability and accuracy of machine learning algorithms used in statistical classification and regression.
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Correlation and dependence
In statistics, dependence or association is any statistical relationship, whether causal or not, between two random variables or bivariate data.
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Cross-validation (statistics)
Cross-validation, sometimes called rotation estimation, or out-of-sample testing is any of various similar model validation techniques for assessing how the results of a statistical analysis will generalize to an independent data set.
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Decision tree
A decision tree is a decision support tool that uses a tree-like graph or model of decisions and their possible consequences, including chance event outcomes, resource costs, and utility.
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Decision tree learning
Decision tree learning uses a decision tree (as a predictive model) to go from observations about an item (represented in the branches) to conclusions about the item's target value (represented in the leaves).
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Donald Geman
Donald Jay Geman (born September 20, 1943) is an American applied mathematician and a leading researcher in the field of machine learning and pattern recognition.
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Ensemble learning
In statistics and machine learning, ensemble methods use multiple learning algorithms to obtain better predictive performance that could be obtained from any of the constituent learning algorithms alone.
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Feature (machine learning)
In machine learning and pattern recognition, a feature is an individual measurable property or characteristic of a phenomenon being observed.
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Generalization error
In supervised learning applications in machine learning and statistical learning theory, generalization error (also known as the out-of-sample error) is a measure of how accurately an algorithm is able to predict outcome values for previously unseen data.
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Gradient boosting
Gradient boosting is a machine learning technique for regression and classification problems, which produces a prediction model in the form of an ensemble of weak prediction models, typically decision trees.
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Independent and identically distributed random variables
In probability theory and statistics, a sequence or other collection of random variables is independent and identically distributed (i.i.d. or iid or IID) if each random variable has the same probability distribution as the others and all are mutually independent.
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K-nearest neighbors algorithm
In pattern recognition, the k-nearest neighbors algorithm (k-NN) is a non-parametric method used for classification and regression.
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Kernel method
In machine learning, kernel methods are a class of algorithms for pattern analysis, whose best known member is the support vector machine (SVM).
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Kullback–Leibler divergence
In mathematical statistics, the Kullback–Leibler divergence (also called relative entropy) is a measure of how one probability distribution diverges from a second, expected probability distribution.
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Lecture Notes in Computer Science
Springer Lecture Notes in Computer Science (LNCS) is a series of computer science books published by Springer Science+Business Media (formerly Springer-Verlag) since 1973.
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Leo Breiman
Leo Breiman (January 27, 1928 – July 5, 2005) was a distinguished statistician at the University of California, Berkeley.
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Linear subspace
In linear algebra and related fields of mathematics, a linear subspace, also known as a vector subspace, or, in the older literature, a linear manifold, is a vector space that is a subset of some other (higher-dimension) vector space.
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Lipschitz
Lipschitz or Lipshitz is an Ashkenazi Jewish surname, which may be derived from the Polish city of Głubczyce (German: Leobschütz), The surname has many variants, including: Lifshitz (Lifschitz), Lifshits, Lifshuts, Lefschetz; Lipschitz, Lipshitz, Lipshits, Lipschutz (Lipschütz), Lipshutz, Lüpschütz; Libschitz; Livshits; Lifszyc, Lipszyc.
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Machine Learning (journal)
Machine Learning is a peer-reviewed scientific journal, published since 1986.
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Mode (statistics)
The mode of a set of data values is the value that appears most often.
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Multinomial logistic regression
In statistics, multinomial logistic regression is a classification method that generalizes logistic regression to multiclass problems, i.e. with more than two possible discrete outcomes.
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Naive Bayes classifier
In machine learning, naive Bayes classifiers are a family of simple "probabilistic classifiers" based on applying Bayes' theorem with strong (naive) independence assumptions between the features.
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Neural Computation (journal)
Neural Computation is a monthly peer-reviewed scientific journal covering all aspects of neural computation, including modeling the brain and the design and construction of neurally-inspired information processing systems.
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Nonparametric statistics
Nonparametric statistics is the branch of statistics that is not based solely on parameterized families of probability distributions (common examples of parameters are the mean and variance).
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Orange (software)
Orange is an open-source data visualization, machine learning and data mining toolkit.
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Out-of-bag error
Out-of-bag (OOB) error, also called out-of-bag estimate, is a method of measuring the prediction error of random forests, boosted decision trees, and other machine learning models utilizing bootstrap aggregating (bagging) to sub-sample data samples used for training.
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Overfitting
In statistics, overfitting is "the production of an analysis that corresponds too closely or exactly to a particular set of data, and may therefore fail to fit additional data or predict future observations reliably".
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Partial permutation
In combinatorial mathematics, a partial permutation, or sequence without repetition, on a finite set S is a bijection between two specified subsets of S. That is, it is defined by two subsets U and V of equal size, and a one-to-one mapping from U to V. Equivalently, it is a partial function on S that can be extended to a permutation.
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R (programming language)
R is a programming language and free software environment for statistical computing and graphics that is supported by the R Foundation for Statistical Computing.
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Random forest
Random forests or random decision forests are an ensemble learning method for classification, regression and other tasks, that operate by constructing a multitude of decision trees at training time and outputting the class that is the mode of the classes (classification) or mean prediction (regression) of the individual trees.
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Random subspace method
In machine learning the random subspace method, also called attribute bagging or feature bagging, is an ensemble learning method that attempts to reduce the correlation between estimators in an ensemble by training them on random samples of features instead of the entire feature set.
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Randomized algorithm
A randomized algorithm is an algorithm that employs a degree of randomness as part of its logic.
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Regression analysis
In statistical modeling, regression analysis is a set of statistical processes for estimating the relationships among variables.
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Scikit-learn
Scikit-learn (formerly scikits.learn) is a free software machine learning library for the Python programming language.
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Statistical classification
In machine learning and statistics, classification is the problem of identifying to which of a set of categories (sub-populations) a new observation belongs, on the basis of a training set of data containing observations (or instances) whose category membership is known.
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Tin Kam Ho
Tin Kam Ho is a computer scientist at IBM research with contributions to machine learning, data mining, and classification.
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Trademark
A trademark, trade mark, or trade-markThe styling of trademark as a single word is predominantly used in the United States and Philippines only, while the two-word styling trade mark is used in many other countries around the world, including the European Union and Commonwealth and ex-Commonwealth jurisdictions (although Canada officially uses "trade-mark" pursuant to the Trade-mark Act, "trade mark" and "trademark" are also commonly used).
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Training, test, and validation sets
In machine learning, the study and construction of algorithms that can learn from and make predictions on data is a common task.
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Trevor Hastie
Trevor John Hastie (born 27 June 1953) is a South African and American statistician and computer scientist.
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Wikipedia
Wikipedia is a multilingual, web-based, free encyclopedia that is based on a model of openly editable content.
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Kernel random forest, Random Forest, Random forests, Random multinomial logit, Random naive Bayes, Random naive bayes.
References
[1] https://en.wikipedia.org/wiki/Random_forest
