What is the purpose of a validation set in machine learning?

Training set is the dataset from which the algorithm learns the patterns and relationships between multiple factors. Training dataset should well represent different sections of the population, else it will be biased and the algorithm build on it will also provide biased results. Also we have to see that the training dataset is relevant. For example if we are working on weekly time series data of sales, and if the business has changed over time, then time series data which is older than certain period will not be relevant. There are statistical ways to check till when data is relevant. Also one can check with the business SME on the same topic.

In machine learning, the training set is used to train a model by providing examples with known inputs and corresponding outputs. The model learns patterns and relationships within the data, enabling it to make predictions or classifications on new, unseen data. The quality and representativeness of the training set significantly impact the model's performance.

The validation set in machine learning serves several important purposes: 1. The validation set helps in selecting the best-performing model among different candidate models or configurations. 2. Validation set helps to detect overfitting by providing an independent dataset for assessing how well the model generalizes beyond the training data. 3. Machine learning models often have hyperparameters. Validation set is used to tune these hyperparameters, ensuring the model's optimal performance without overfitting to the training set. 4. By assessing metrics like accuracy, precision, recall, or others on the validation set, we can understand the models performance on unseen data.

One of the important steps in the model prepration stage is to split data into Training set, Validation set and Test set. Validation set is there to help you measure the performance of your model on some out of sample data it has never seen, allowing you to fine tune it, or decide between it and alternative models. Why not just split into Training set and Test set and use that instead you ask? Well, it turns out, you can actually overfit your data manually by fine tuning your model iteratively. This is why we keep a part of the data as Test set, for you to use on your final model, after you're done with all your fine tunings. Validation set is your playground. After using your model on the Test set, you shouldn't touch it anymore :).

Algorithms can tell you when they've got good at finding patterns in a training data set, but does that pattern reflect the forces shaping the data (which is what you want), or has it just got good at matching the random quirks of the data it saw? You can't tell from just looking at how well it fits the training data, all you know is that it does ok with those numbers it's already seen. So you need new data that it hasn't seen before to test it on. This new data should be shaped by the same underlying forces as the first set, but it won't share whatever random quirks the training set had. So if your algorithm does a good job with this data it hasn't seen before you get more confident that it has detected something real and actually useful.

Testing dataset is used to check the performance of the model. Data scientist's objective is that when a model is deployed, it should be able to predict the outcomes for new observations accurately, which it has not seen. The model is built on the training dataset and the testing dataset is not part of it. Now when the model is used to predict the outcomes of testing dataset, records of which the model has not seen earlier, the accuracy of predictions gives us an understanding how accurately the model can predict when it is deployed. To be more confident about performance of the model post deployment, we build model on different training datasets and check it's performance of different testing datasets.

1. The testing set provides an unbiased evaluation of the model's performance on completely new and unseen data. This ensures that the assessment reflects the model's ability to generalize beyond the examples it was trained on. 2. It is used to calculate and report the final performance metrics of the model. 3. It helps determine how well the model generalizes to data it has never encountered before. This is crucial for understanding whether the model has learned meaningful patterns from the data or not. 4. The results on the testing set guide the decision of whether the model is ready for deployment in a real-world environment. It is more likely to work in real world.

Think of fitting like teaching a student. Overfitting is like a student who crams so much they remember every tiny detail in the textbook, even the typos. They ace the class tests but struggle in real-world situations because they can't adapt. That's like a model that learns the training data too well, including its quirks, and fails on new data. Underfitting, on the other hand, is like a student who only learns the basics and misses key concepts. They perform poorly in both class tests and real life. This happens when a model is too simple and misses important patterns in the data. Using a validation set is like giving practice tests to find the right balance in studying - not too much, not too little, just right for real-world challenges.

It can be used as warning sign about overfitting or underfitting about the model. As we know that validation set is pre-testing evaluation of model performance, if your validaton accuracy is good and validation loss is good as well, you can have some confidance in your model performance, why? because now model is able to generelized it self well on unseen data.

In summary, the validation set helps manage the trade-off between overfitting and underfitting in machine learning by: - Detecting overfitting through independent evaluation. - Indicating underfitting by assessing model performance. - Guiding adjustments to model complexity. - Facilitating hyperparameter tuning to optimize generalization.

To split data for machine learning: 1. Train-Validation-Test Split: - Divide data into training, validation, and test sets, often in proportions like 70-15-15%. - Use this split to train the model, tune hyperparameters, and evaluate its final performance. 2. Cross-Validation: - Implement techniques like K-Fold Cross-Validation to iteratively train and validate the model on different subsets of the data. - This provides a more robust assessment of the model's performance. 3. Stratified Sampling: - Ensure that the distribution of classes is maintained in each split. - Particularly useful for datasets with imbalanced class distribution.

We use validation dataset to finalize model which we will be deployed. We change different hyperparameters/features and observe with which model provides higher performance. We often use different combinations of training and validation datasets and then compare accuracy of models. This gives us more confidence that the model with a specific hyper parameter we are selecting can perform well in different situations. There are times when different models perform well in different validation datasets. That means we don't have one model which is a clear winner. During those situations, we carefully ensemble some models. The predictions from multiple models are combined to provide the final prediction.

A validation set is essential in training ML models. Here's a quick rundown: Data Splitting: Divide data into three: training (to train), validation (for tuning & assessing), and test (for final evaluation). Model Training: Train the model using the training set to learn data patterns. Performance Assessment: Use the validation set to evaluate the model's performance and tweak settings. Hyperparameter Tuning: Fine-tune the model based on validation set performance. Final Evaluation: Assess the model's performance on the separate test set for unbiased results. Iterate as Needed: Adjust the model based on validation performance until desired results.

In the context of Deep Learning models, the validation set serves as a crucial tool for assessing the performance of the model during the training process. Similarly, in the realm of Machine Learning, the validation set also plays a vital role in swiftly evaluating the model's performance. The primary purpose of the validation set is to offer a concise and representative sample of data that can be used to gauge the effectiveness of the model. This becomes particularly advantageous when dealing with extensive datasets, as it allows for rapid conclusions to be drawn in terms of model selection.

What if your dataset is too small for you to be able to afford a Validation set? You still need it. You can't use the Test set instead. Well, in that case, we use a technique called K-fold Cross-Validation. First you take out your Test set and put it aside. Then you split the what's left into for example 5 chunks (called folds). Then, you train your model on 4 of them, and validate it using the 5th, which gives you a training score and a validation score. You repeat the above 5 times for every 4v1 combination of the data you have, giving you 5 training scores and 5 validation scores for each fold. You take the mean of these, and there you have your final scores, without giving up on your data for training.