You work as a machine learning specialist at a hedge fund firm.
Your firm is working on a new quant algorithm to predict when to enter and exit holdings in their portfolio.
You are building a machine learning model to predict these entry and exit points in time.
You have cleaned your data, and you are now ready to split the data into training and test datasets. Which splitting technique is best suited to your model's requirements?
Click on the arrows to vote for the correct answer
A. B. C. D.Answer: B.
Option A is incorrect.
Using k-fold cross validation will randomly split your data.
But you need to consider the time-series nature of your data when splitting.
So randomizing the data would eliminate the time element of your observations, making the datasets unusable for predicting price changes over time.
Option B is correct.
By sequentially splitting the data, you preserve the time element of your observations.
Option C is incorrect.
Randomly splitting the data would eliminate the time element of your observations, making the datasets unusable for predicting price changes over time.
Option D is incorrect.
If you split the data by a category such as the holding attribute, you would create imbalanced training and test dataset since some holdings would only be in the training dataset and others would only be in the test dataset.
Reference:
Please see the Amazon Machine Learning developer guide titled Splitting Your Data.
When splitting a dataset into training and testing sets, the main goal is to ensure that the model is trained on a representative sample of the data, and that it is evaluated on a completely separate and independent dataset to accurately assess its performance.
In this scenario, where the goal is to build a machine learning model to predict entry and exit points in a hedge fund portfolio, we need to consider the nature of the data and the specific requirements of the model.
K-fold cross-validation is a technique where the dataset is divided into k subsets of equal size, and the model is trained and evaluated k times, with each subset used as the testing set once, and the remaining subsets used for training. This technique can be useful when the dataset is small and the goal is to get the most out of the available data. However, it may not be the best choice in this scenario, as the dataset size is not mentioned, and the training and testing datasets should ideally be representative of the overall distribution of the data, which may not be achieved with k-fold cross-validation.
Sequential splitting is a technique where the dataset is split into training and testing datasets by selecting a specific point in time, such as using the first 80% of the data for training and the remaining 20% for testing. This technique may be useful in certain scenarios where the data has a temporal aspect, such as financial data, and we want to evaluate the model's ability to predict future events based on past data. However, this technique may not be suitable if there is a large variation in the data over time or if the temporal aspect is not relevant to the problem.
Random splitting is a technique where the dataset is randomly divided into training and testing datasets, with a certain percentage of the data used for each. This technique is commonly used in machine learning and is generally suitable for a wide range of datasets, including financial data. Random splitting ensures that the distribution of the data is evenly represented in both the training and testing datasets, which helps to minimize the risk of overfitting the model to the training data.
Categorical splitting, as described in option D, is not a commonly used technique in machine learning and is not relevant to this scenario.
In conclusion, the most appropriate splitting technique for this scenario is likely to be random splitting, as it allows for an even distribution of the data in the training and testing datasets, which is important for ensuring that the model is trained and evaluated on representative data.