How do you choose between a holdout and a validation set in machine learning?

I don't think this terminology is accurate. Usually you have a training set (trains each model) a validation set (picks the best model) and a holdout set (evaluates the best chosen model).

Choosing the holdout method for model evaluation is setting a gold standard for performance assessment. This involves segregating data into a training set and a holdout set—the latter remains untouched during training. The holdout set should encapsulate real-world data variability to truly test the model’s generalizability. Ensuring this subset is representative of the larger dataset is vital to prevent biased performance metrics. Experts may employ stratification to maintain consistent distributions, thus safeguarding the evaluation’s accuracy. Though efficient, one must balance well to avoid overfitting, ensuring the model's robustness in actual application scenarios.

This is the most common method and the first method beginners will be introduced to. It simply splits the dataset into two: Training set and Test set. The Training set is usually 70 or 80% of the dataset while the Test set is usually 20 or 30%.

Traditionally, 80-20% split is used to split the training and validation datasets respectively. However, 20% of data reserved for validation, does not necessarily represent all the data combinations identified in 80% of the data reserved for training. Hence, the best approach is to identify the validation dataset based on data combinations rather than split the data. For any data combinations not available in the validation dataset should be generated synthetically. This will ensure high test coverage and comprehensive quality evaluation of the AI model. Also, variations of test data should also be generated to validate the sensitivity of the AI models. This will prevent bias and sensitivity of the AI models to certain data variations.

The holdout method is a simple way to split your data into two sets: a training set and a test set. The training set is where you prepare your model and make it better. Once you're confident your model is good, you use the test set to check how well it works on new data. Choosing the right size for the test set is important. You want it to be big enough to give a good idea of how your model performs but not so big that you don't have enough data to practice with. People often use a rule of 80% for training and 20% for testing, but you can change this to fit your data better. The goal is to find a balance that helps you evaluate your model well and still have plenty of data to train it effectively.

The validation method involves setting aside a portion of your data for fine-tuning your model's hyperparameters and choosing the best model before testing it on a separate test set. It helps ensure your model is well-adjusted and effective before assessing its performance on unseen data, striking a balance between training and evaluatio.K-Fold Cross-Validation is one of strategy for validation which involves dividing your dataset into "k" equally sized subsets or folds. You train and validate your model "k" times, each time using a different fold as the validation set and the remaining folds as the training set

Here are tips to make sure that your data is both representative and independent of your training data: - Use the same data source(s) for both validation and training - Stratify your sets, so that the target variable is distributed similarly - To ensure indepency, group data based on time or location and split based on that grouping. For example train on 2000-2018 and test on 2019-2023. Sci-kit learn contains tools that can help with this in the model_selection package, like GroupKFold and StratifiedKFold.

The holdout method is a simple approach to split the data for evaluating AI models, but it can be problematic for small or imbalanced datasets. On the other hand, the validation method offers more flexibility and robustness, but it comes with computational complexities. Despite the complexities of the validation method, it is preferred to ensure the reliability and consistency of the AI model. It is worth putting more effort into development. The validation split method can help reduce the model's bias and variance. It helps to prevent overfitting by providing a separate set of data for evaluation. It provides an unbiased estimate of the model's performance on unseen data, giving a more realistic assessment of its generalizability.

Validation is the process of assessing a model's performance on an independent dataset. It aids alot in tuning hyperparameters to optimize the model's generalization. Common methods include k-fold cross-validation, where the dataset is divided into k subsets for iterative training and testing. This helps gauge how well the model will perform on unseen data.

The validation method introduces a three-way split, with training, validation, and test sets. The training set educates your model, the validation set aids in fine-tuning hyperparameters, and the test set remains the final arbiter of model evaluation. This method allows for iterative adjustments based on validation set performance, optimizing your model for better generalization.

Leveraging the holdout method in model validation is efficient and straightforward, bypassing the computational load of cross-validation. Yet, it might not fully reflect the model's stability across diverse data samples, potentially misrepresenting its performance. Cross-validation, through its iterative process, provides a comprehensive performance estimate but at a higher computational cost. Selecting between these methods necessitates a keen insight into the dataset's uniqueness and computational feasibility.

The validation method may also be impractical simply if there is insufficient representative data to generate a dataset exclusively for validation. One example is classification with a minority class that is severely underrepresented. Without generating synthetic data you may be hard pressed to find enough to create representative sets.

Holdout set is easy to understand and implement, making it a good choice for quick model assessment. It ensures that training on a smaller portion of the data is faster. But from my experience I can say that there is a risk of overfitting ,if the holdout set is too small, the model may not generalize well. Also, the performance estimate with holdout set can vary depending on how data is randomly split. Validation set is ideal for optimizing model settings and selecting the best model architecture.It provides a more stable performance estimate by considering multiple subsets. But setting up cross-validation can be more involved, especially with limited data. And, running multiple iterations of model training and validation takes more time

Holdout Set: Pros - Simple to implement, provides an unbiased estimate of model performance. Cons - Limited data for training, susceptible to variability due to a single random split. Validation Set: Pros - Facilitates hyperparameter tuning, helps prevent overfitting during model development. Cons - May introduce bias if not representative, requires careful separation from training data.

Validation set also depends on the type of problem we are encountering, for time series we can't follow to randomly extract a percentage of data points from our data for holdout set, we have to consider the data with respect to the events occurrence and spaced time difference.

In machine learning, the choice between a holdout and a validation set is determined by the specific needs of our projects. When you have a huge dataset and can spare a considerable chunk for testing, you should utilise a holdout set. It is a one-time test to ensure the generalisation of your model. A validation set, on the other hand, is utilised for iterative model adjustment, particularly when the dataset is limited. You train and evaluate your model frequently to fine-tune its parameters and performance. So, if you have a lot of data and want a quick review, go with a holdout set. Choose a validation set if you have little data and need iterative improvements. In my experience, depending on the situation, I have utilised both ways.

In situations where you're dealing with highly imbalanced data in a classification problem, opting for a train-test-validation split can be advantageous. This approach enables more robust model evaluation and hyperparameter tuning. Additionally, using the F1 score as the primary evaluation metric is recommended because it strikes a balance between precision and recall, leading to a more effective and generalized fit on new data.

Choosing between a holdout set and validation set pivots on data volume and diversity. With ample data, a holdout set can suffice, preserving computational resources. However, for smaller datasets, or when model stability is paramount, k-fold cross-validation can exploit the data more effectively, albeit with increased computational overhead. A balanced approach often entails using a holdout set for initial assessment, followed by cross-validation for fine-tuning, ensuring robust model evaluation while managing resources adeptly. This dual strategy harnesses both methods' strengths, catering to pragmatic and precise model validation.

In my opinion, in the vast majority of cases you'll encounter in a business context, you should be able to use both methods. Using cross validation during training / hyperparameter optimization, while retaining a holdout set for your final evaluation is the gold standard if you have enough data and computational resources. This will generally be doable and preferable for the majority of medium-sized datasets that are most often encountered in the workplace - somewhere in the thousands to millions of rows.

Validation methods often provide more accurate and generalizable results, especially in heterogeneous data or complex models. Cross-validation and bootstrapping reduce overfitting risks in small or imbalanced datasets, where the holdout method may be less reliable. However, it's essential to consider various factors related to the data, model, and resource constraints when choosing between the holdout and validation methods. Choose holdout method for large, homogeneous datasets and the validation methods for small or diverse datasets. Choose holdout method for simpler, stable models and validation methods for complex, highly variable models. Choose holdout method If resources and time are limited and validation methods with more resources.

Whether you choose between holdout and validation methods in machine learning, repeating the splitting process multiple times is crucial. Below are the primary reasons for doing this: - Randomly splitting data into training and testing sets introduces variability in the evaluation metrics. Repeating the process gives a more stable and representative model performance estimate. - Repeating the splitting process allows us to assess how well the model generalizes to different subsets of the data. - Multiple repetitions enable us to compute statistical measures such as means and standard deviations for evaluation metrics, providing a more comprehensive understanding of the model's performance.

An optimal subsampling approach to obtain train, test and validation dataset is to use Support Points according to the energy distance criterion and a sequencial nearest neighbors algorithm as shown by V. Roshan. Integrate this OR problem as a part of a paired extended decision analysis allow to take into account the efficiency of challenging deterministics multiple splitting approach both by the relationships between data and the environment where their are product and the one between data sources.

In the realm of machine learning, the journey from data to model involves strategic decisions. The choice between a holdout and a validation set is one such decision, and understanding the landscape of your data, model, and computational resources will guide you in selecting the path that leads to optimal model performance and generalization.