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Model Selection with Cross-validations and Bootstraps Application to Time Series Prediction with BFN Models 112Amaury Lease, Vincent Hertz, and Michel Verlaine 1Universit Catholic de Lou vain SESAME,

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What is model selection with cross-validations?

Model selection with cross-validations is a technique used in machine learning to select the best model among a set of candidate models. It involves dividing the available data into multiple subsets, called folds, and training and evaluating the models on different combinations of these folds to obtain an unbiased estimate of their performance.

Who is required to file model selection with cross-validations?

Model selection with cross-validations is typically performed by data scientists, machine learning engineers, or researchers who are developing and evaluating machine learning models.

How to fill out model selection with cross-validations?

To fill out model selection with cross-validations, you need to follow these steps: 1) Split your data into several folds, usually using techniques like k-fold or stratified k-fold cross-validation. 2) Train your model on a subset of the folds and evaluate its performance on the remaining fold. 3) Repeat this process multiple times, changing the subsets of folds used for training and evaluation. 4) Analyze the performance metrics obtained for each model to select the best one.

What is the purpose of model selection with cross-validations?

The purpose of model selection with cross-validations is to choose the most suitable model for a given problem or dataset. It helps in determining which model has the best performance in terms of metrics such as accuracy, precision, recall, or mean squared error. By using cross-validation, it is possible to reduce the bias in estimating the performance of the models and select the one that is less likely to overfit or underfit the data.

What information must be reported on model selection with cross-validations?

In a model selection with cross-validations report, it is important to include information such as the choice of validation technique (e.g., k-fold cross-validation), the performance metrics used to evaluate the models, the dataset used for training and evaluation, the hyperparameters of the models tested, and the final performance comparison between the models.