3. Methodology for implementing a machine learning model
3.1 Training and validation
The first step is to train an algorithm with its default hyperparameters. To do this, simply take 70% or 80% of the data set for training and keep the rest for testing. This distribution depends on the size of the available dataset: you need to have enough data for training, while keeping a dataset of an acceptable size for testing, and therefore for checking the model's generalization capacity.
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