You work as a machine learning specialist for a financial services company.
You are building a machine learning model to perform futures price prediction.
You have trained your model, and you now want to evaluate it to make sure it is not overtrained and can generalize. Which of the following techniques is the appropriate method to cross-validate your machine learning model?
Click on the arrows to vote for the correct answer
A. B. C. D.Answer: D.
Option A is incorrect.
Since we are trying to validate a time series set of data, we need to use a method that uses a rolling origin with day n as training data and day n+1 as test data.
The LOOCV approach doesn't give us this option.
(See the article K-Fold and Other Cross-Validation Techniques)
Option B is incorrect.
The K-Fold cross validation technique randomizes the test dataset.
We cannot randomize our test dataset since we try to validate a time series set of data.
Randomized time series data loses its time-related value.
Option C is incorrect.
We are trying to cross-validate time series data.
We cannot randomize the test data because it will lose its time-related value.
Option D is correct.
The Time Series Cross Validation technique is the correct choice for cross-validating a time series dataset.
Time series cross validation uses forward chaining, where the origin of the forecast moves forward in time.Day n is training data and day n+1 is test data.
Reference:
Please see the Amazon Machine Learning developer guide titled Cross Validation, and the article K-Fold and Other Cross-Validation Techniques.
Cross-validation is a technique to evaluate the performance of a machine learning model by testing it on an independent dataset. It is used to check the ability of a model to generalize to unseen data.
In this scenario, the appropriate method to cross-validate the machine learning model is K-Fold Cross Validation (option B).
K-Fold Cross Validation is a common technique used for model evaluation. In K-Fold Cross Validation, the dataset is divided into K folds, where K-1 folds are used for training, and the remaining fold is used for testing. This process is repeated K times, with each fold being used for testing once. The results of each fold are averaged to obtain an overall estimate of the model's performance.
The advantage of K-Fold Cross Validation is that it uses all the data for both training and testing, and the results are less biased than other methods, such as hold-out validation.
Leave One Out Cross Validation (LOOCV) (option A) is a type of K-Fold Cross Validation, where K is equal to the number of data points in the dataset. This method is computationally expensive, and it can be sensitive to outliers.
Stratified Cross Validation (option C) is used when the dataset is imbalanced. It ensures that the class distribution in the training and testing sets is similar to the class distribution in the overall dataset.
Time Series Cross Validation (option D) is used when dealing with time series data, where the data is ordered chronologically. In this method, the dataset is divided into training and testing sets based on time. The model is trained on data up to a certain point in time and tested on data after that point.
In conclusion, K-Fold Cross Validation (option B) is the appropriate method to cross-validate the machine learning model in this scenario. It is a widely used technique that uses all the data for training and testing, and it provides less biased results than other methods.