holdoutmenetelmässä

The holdout method is a validation technique used in machine learning and statistical modeling to assess the performance of a model. It involves partitioning a dataset into two subsets: a training set and a testing set. The training set is used to train the model, allowing it to learn patterns and relationships within the data. Once the model has been trained, its performance is evaluated on the testing set, which it has not seen during the training phase. This evaluation provides an unbiased estimate of how well the model is likely to generalize to new, unseen data.

The holdout method is a straightforward and commonly used approach. The size of the split between the training and testing sets can vary, but a typical split might be 70% for training and 30% for testing, or 80% for training and 20% for testing. The key principle is that the testing set acts as a proxy for future data, and the model's accuracy on this set indicates its predictive power. If a model performs very well on the training set but poorly on the testing set, it suggests that the model may be overfitting the training data, meaning it has learned the training data too specifically and is not generalizing well. Conversely, if it performs poorly on both, it might be underfitting. While simple, the holdout method can be sensitive to the specific split of the data; a different random split might lead to a different performance estimate.