What are the best practices for selecting the random_state parameter in train_test_split when analyzing cryptocurrency market data?

1 answers

• Apr 30, 2022 · 3 years ago
  When it comes to selecting the random_state parameter in the train_test_split function for analyzing cryptocurrency market data, it's important to consider the specific requirements of your analysis. The random_state parameter is used to control the random splitting of the data into training and testing sets. By setting a specific random_state value, you can ensure that the same split is obtained every time you run the code. This can be useful for reproducibility and comparison purposes. However, it's worth noting that the choice of random_state value should not have a significant impact on the overall results of your analysis. As long as you choose a value that is appropriate for your specific needs and ensures consistent results, you should be able to effectively analyze your cryptocurrency market data. Remember to experiment with different random_state values and evaluate the performance of your model on each split to find the best fit for your analysis.
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