Categories → Causal Wizard Concept , Validation , Statistics , Study Design , Method
Validation is the process of evaluating model performance on new, unseen data to ensure it generalizes well and to identify potential issues such as overfitting and bias
Validation in machine learning refers to the process of evaluating the performance of a trained model on a separate dataset that was not used during the training process. The goal of validation is to assess how well the model generalizes to new, unseen data, and to identify any potential issues such as overfitting or underfitting.
Validation is important in machine learning because it helps to ensure that the model is not simply memorizing the training data, but is instead learning meaningful patterns that can be applied to new data. In other word, we are validating that the model can generalise to new, unseen data. Overfitting occurs when a model becomes too complex and starts to fit noise in the training data, resulting in poor generalisation performance, on new data. Underfitting occurs when a model is too simple and cannot capture the underlying patterns in the data, also leading to poor performance.
Validation techniques include hold-out validation, k-fold cross-validation, and leave-one-out cross-validation, among others. These methods involve partitioning the data into training and validation sets, and evaluating the performance of the model on the validation set. The choice of validation technique depends on the size of the dataset, the complexity of the model, and other factors.
Bootstrap validation is a resampling method used to estimate the accuracy of a machine learning model. It involves randomly selecting samples with replacement from the original dataset to create multiple training and validation sets. The model is trained on each of these sets, and the performance metrics are calculated and averaged across all iterations. Bootstrap validation is particularly useful when the dataset is small or when there are limited resources for data collection, as it allows for a more robust estimate of model performance without requiring additional data.
Causal Wizard uses several validation techniques to refute the results, and provide an understanding of the significance, robustness, sensitivity, and generality of your results.
Validation is a critical step in the machine learning workflow as it helps to ensure that the model is accurate, robust, and reliable, and can be used effectively in real-world applications.