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If cross-validation is used to decide which features to use, an inner cross-validation to carry out the feature selection on every training set must be performed. [30] Performing mean-centering, rescaling, dimensionality reduction, outlier removal or any other data-dependent preprocessing using the entire data set.
A training data set is a data set of examples used during the learning process and is used to fit the parameters (e.g., weights) of, for example, a classifier. [9] [10]For classification tasks, a supervised learning algorithm looks at the training data set to determine, or learn, the optimal combinations of variables that will generate a good predictive model. [11]
In machine learning (ML), a learning curve (or training curve) is a graphical representation that shows how a model's performance on a training set (and usually a validation set) changes with the number of training iterations (epochs) or the amount of training data. [1]
Cross-Validation Selection can be summed up as: "try them all with the training set, and pick the one that works best". [32] Gating is a generalization of Cross-Validation Selection. It involves training another learning model to decide which of the models in the bucket is best-suited to solve the problem.
The amount of overfitting can be tested using cross-validation methods, that split the sample into simulated training samples and testing samples. The model is then trained on a training sample and evaluated on the testing sample.
Filter feature selection is a specific case of a more general paradigm called structure learning.Feature selection finds the relevant feature set for a specific target variable whereas structure learning finds the relationships between all the variables, usually by expressing these relationships as a graph.
Cross-validation. By splitting the data into multiple parts, we can check if an analysis (like a fitted model) based on one part of the data generalizes to another part of the data as well. [144] Cross-validation is generally inappropriate, though, if there are correlations within the data, e.g. with panel data. [145]
Cross-validation (statistics)#Nested cross-validation; This page is a redirect. The following categories are used to track and monitor this redirect: