Is the usually recommended data split between training and evaluation close to 80% to 20% correspondingly?
The usual split between training and evaluation in machine learning models is not fixed and can vary depending on various factors. However, it is generally recommended to allocate a significant portion of the data for training, typically around 70-80%, and reserve the remaining portion for evaluation, which would be around 20-30%. This split ensures that the model is trained on a sufficiently large dataset while also allowing for an independent evaluation of its performance.
The allocation of data for training and evaluation serves several purposes. Firstly, it helps in assessing the model's ability to generalize to unseen data. By evaluating the model on a separate dataset, we can get an unbiased estimate of its performance and understand how well it is likely to perform in real-world scenarios. Secondly, it helps in preventing overfitting, which is a common problem in machine learning. Overfitting occurs when a model becomes too complex and starts to memorize the training data rather than learning the underlying patterns. By evaluating the model on unseen data, we can identify if it is overfitting and take appropriate measures to address it.
The choice of the split between training and evaluation data should be based on the specific requirements of the problem at hand. If the dataset is large, it may be possible to allocate a smaller percentage for evaluation without compromising the model's performance. On the other hand, if the dataset is small, it becomes important to have a larger portion reserved for evaluation to obtain reliable performance estimates. Additionally, the complexity of the problem and the availability of labeled data also play a role in determining the split. For complex problems or when labeled data is scarce, it is advisable to allocate a larger portion for training to ensure that the model can learn the underlying patterns effectively.
To illustrate this, let's consider an example. Suppose we are training a machine learning model to classify images of cats and dogs. We have a dataset of 10,000 images, and we decide to allocate 80% (8,000 images) for training and 20% (2,000 images) for evaluation. The model is trained on the 8,000 images, and its performance is evaluated on the remaining 2,000 images. This split allows us to assess how well the model can classify unseen images and provides an estimate of its accuracy in real-world scenarios.
While the usual split between training and evaluation in machine learning models is not fixed, a common practice is to allocate around 80% (sometimes towards 70%) of the data for training and reserve the remaining 20 (sometimes towards 30%) for evaluation. This split helps in assessing the model's ability to generalize, preventing overfitting, and obtaining reliable performance estimates. However, the specific allocation should be based on the requirements of the problem, the size of the dataset, the complexity of the problem, and the availability of labeled data.
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