In Artificial Intelligence (AI) and computer vision, data plays a very important role. The selected data will have a significant impact on the output of the model. Meanwhile, evaluating the model using data not used in its training is crucial to demonstrate its generalizability.
Another important issue to consider is overfitting the model, which should be avoided at all costs. Overfitting can be prevented using various techniques, but the final assessment of whether the model is overfitted or not should be performed using a separate dataset. That is why it is necessary to divide the dataset into different parts. In general, datasets are divided into three main parts: training, testing, and validation.
The Training Dataset: The model is trained using the training dataset. It is necessary to dedicate a large part of the dataset to train the model. Models are usually trained on 70% or more of the main dataset.
The Validation Dataset: The validation dataset is used to evaluate a model’s fit on the training dataset while tuning model hyperparameters. Validation datasets are used frequently to evaluate a given model and the resulting data is used by machine learning engineers to fine-tune the hyperparameters. Consequently, the model occasionally interacts with validation data, but never learns from it. In other words, the validation set indirectly affects a model.
The Test Dataset: A subset of data used to evaluate the fit of a final model over a training dataset with unbiased results. Model evaluation is based on the Test dataset. When a model has been completely trained (using both train and validation sets), the test dataset is used to evaluate the model’s performance as the final step in the process.
With different dataset division methods, different strategies can be considered for model training. Different strategies are chosen based on various parameters, such as the number of available datasets, the type of the model, the accuracy required during the training process, etc. It is recommended to always use a portion of the dataset for validation and testing during model training and evaluation. When the model is intended to be used in a product, it is best to use zero percent, or the lowest possible value for validation and test datasets to use as much data as possible to train it.
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