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2. Machine learning - Wikipedia

   en.wikipedia.org/wiki/Machine_learning

   Characterizing the generalization of various learning algorithms is an active topic of current research, especially for deep learning algorithms. Statistics. Machine learning and statistics are closely related fields in terms of methods, but distinct in their principal goal: statistics draws population inferences from a sample, while machine ...

3. Statistical learning theory - Wikipedia

   en.wikipedia.org/wiki/Statistical_learning_theory

   Statistical learning theory is a framework for machine learning drawing from the fields of statistics and functional analysis. [1] [2] [3] Statistical learning theory deals with the statistical inference problem of finding a predictive function based on data. Statistical learning theory has led to successful applications in fields such as ...

4. Bias–variance tradeoff - Wikipedia

   en.wikipedia.org/wiki/Bias–variance_tradeoff

   Bias and variance as function of model complexity. In statistics and machine learning, the bias–variance tradeoff describes the relationship between a model's complexity, the accuracy of its predictions, and how well it can make predictions on previously unseen data that were not used to train the model. In general, as we increase the number ...

5. Timeline of machine learning - Wikipedia

   en.wikipedia.org/wiki/Timeline_of_machine_learning

   1950s. Pioneering machine learning research is conducted using simple algorithms. 1960s. Bayesian methods are introduced for probabilistic inference in machine learning. [1] 1970s. ' AI winter ' caused by pessimism about machine learning effectiveness. 1980s.

6. Decision tree learning - Wikipedia

   en.wikipedia.org/wiki/Decision_tree_learning

   v. t. e. Decision tree learning is a supervised learning approach used in statistics, data mining and machine learning. In this formalism, a classification or regression decision tree is used as a predictive model to draw conclusions about a set of observations. Tree models where the target variable can take a discrete set of values are called ...

7. Training, validation, and test data sets - Wikipedia

   en.wikipedia.org/wiki/Training,_validation,_and...

   In particular, three data sets are commonly used in different stages of the creation of the model: training, validation, and test sets. The model is initially fit on a training data set, [3] which is a set of examples used to fit the parameters (e.g. weights of connections between neurons in artificial neural networks) of the model. [4]

8. Boosting (machine learning) - Wikipedia

   en.wikipedia.org/wiki/Boosting_(machine_learning)

   Machine learningand data mining. In machine learning, boosting is an ensemble meta-algorithm for primarily reducing bias, variance. [1] It is used in supervised learning and a family of machine learning algorithms that convert weak learners to strong ones. [2]

9. Regression analysis - Wikipedia

   en.wikipedia.org/wiki/Regression_analysis

   t. e. In statistical modeling, regression analysis is a set of statistical processes for estimating the relationships between a dependent variable (often called the 'outcome' or 'response' variable, or a 'label' in machine learning parlance) and one or more independent variables (often called 'predictors', 'covariates', 'explanatory variables ...