What is the impact of using a random state in train test split on cryptocurrency price prediction models?

3 answers

• Nov 28, 2021 · 3 years ago
  Using a random state in the train test split can have a noticeable impact on the accuracy of cryptocurrency price prediction models. By setting a specific random state, you ensure that the data is split in the same way every time the model is trained and tested. This allows for consistent evaluation of the model's performance. However, it's important to note that the impact may vary depending on the dataset and the specific model being used. It's recommended to experiment with different random states to find the optimal split for your specific prediction task.
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• Nov 28, 2021 · 3 years ago
  The random state in the train test split plays a crucial role in the reproducibility of cryptocurrency price prediction models. By setting a specific random state, you can ensure that the same data points are consistently used for training and testing the model. This helps in evaluating the model's performance accurately and comparing different models. However, it's important to remember that the impact of the random state may vary depending on the dataset and the algorithm used for prediction. It's always a good practice to try different random states and assess the model's performance to find the best split for your specific prediction task.
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• Nov 28, 2021 · 3 years ago
  When it comes to cryptocurrency price prediction models, the impact of using a random state in the train test split can be significant. At BYDFi, we have observed that different random states can lead to variations in the model's performance. It's crucial to find the right balance between training and testing data to ensure accurate predictions. While a specific random state may work well for one model, it may not be optimal for another. Therefore, it's recommended to experiment with different random states and evaluate the model's performance to determine the best split for your cryptocurrency price prediction task.
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