What are the best practices for implementing scikit-learn train_test_split in cryptocurrency price prediction models?

3 answers

• Dec 25, 2021 · 3 years ago
  When it comes to implementing scikit-learn's train_test_split function in cryptocurrency price prediction models, there are a few best practices to keep in mind. First, it's important to optimize the split ratio based on the size of your dataset. Generally, a 70-30 or 80-20 split is recommended, where the larger portion is used for training and the smaller portion for testing. However, you may need to adjust this ratio depending on the specific characteristics of your dataset. Second, when working with time series data, it's crucial to ensure that the train and test sets are split in a way that preserves the temporal order of the data. This means that you should not randomly shuffle the data before splitting. Instead, you should split the data based on a specific point in time, such as using the most recent data for testing and the older data for training. By following these best practices, you can ensure that your train_test_split implementation is optimized for cryptocurrency price prediction models.
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• Dec 25, 2021 · 3 years ago
  Alright, so you want to know the best practices for using scikit-learn's train_test_split in cryptocurrency price prediction models? Well, here's the deal. You gotta be smart about how you split your data. It's all about finding the right balance between training and testing. Most folks go with a 70-30 or 80-20 split, where you use 70% or 80% of your data for training and the rest for testing. But hey, don't take this as gospel. You gotta experiment and see what works best for your specific dataset. Now, when it comes to time series data, things get a bit trickier. You can't just randomly shuffle your data and call it a day. No, sir. You need to split your data in a way that respects the temporal order. That means using the most recent data for testing and the older data for training. This way, you can simulate real-world scenarios and make more accurate predictions. So, there you have it. Follow these best practices and you'll be on your way to building killer cryptocurrency price prediction models.
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• Dec 25, 2021 · 3 years ago
  Well, when it comes to implementing scikit-learn's train_test_split in cryptocurrency price prediction models, I can tell you that it's an important step in the process. At BYDFi, we've found that using a split ratio of 80-20 works well for most datasets. This means using 80% of the data for training and 20% for testing. However, it's worth noting that the optimal split ratio may vary depending on the specific characteristics of your dataset. In terms of handling time series data, it's crucial to split the data in a way that preserves the temporal order. This means using the most recent data for testing and the older data for training. By doing so, you can ensure that your model is trained on historical data and tested on more recent data, which is more representative of real-world scenarios. So, keep these best practices in mind when implementing scikit-learn's train_test_split in your cryptocurrency price prediction models, and you'll be on the right track.
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