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Cross-validation, [2] [3] [4] sometimes called rotation estimation ... When many different statistical or machine learning models are being considered, ...
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]
Cross-validation is an alternative that is applicable to non time-series scenarios. Cross-validation involves splitting multiple partitions of the data into training set and validation set – instead of a single partition into a training set and validation set.
In statistics, the jackknife (jackknife cross-validation) is a cross-validation technique and, therefore, a form of resampling. It is especially useful for bias and variance estimation. The jackknife pre-dates other common resampling methods such as the bootstrap.
Evaluate the hyperparameter tuples and acquire their fitness function (e.g., 10-fold cross-validation accuracy of the machine learning algorithm with those hyperparameters) Rank the hyperparameter tuples by their relative fitness; Replace the worst-performing hyperparameter tuples with new ones generated via crossover and mutation
Cross validation is a method of model validation that iteratively refits the model, each time leaving out just a small sample and comparing whether the samples left out are predicted by the model: there are many kinds of cross validation. Predictive simulation is used to compare simulated data to actual data.
List of datasets for machine-learning research. ... Cross-validation (statistics) Random forest; Random subspace method (attribute bagging) References
The first stability condition, leave-one-out cross-validation stability, says that to be stable, ... The Journal of Machine Learning Research. 2: ...