What are the recommended approaches for selecting train-test splits and random states in cryptocurrency analysis?

3 answers

• Dec 27, 2021 · 3 years ago
  When it comes to selecting train-test splits and random states in cryptocurrency analysis, there are a few recommended approaches. One common technique is to use a random sampling method to divide the data into training and testing sets. This ensures that the data is representative of the overall dataset and reduces the risk of bias. Another approach is to use a time-based split, where the data is divided based on a specific time period. This can be useful when analyzing time series data, as it allows for the evaluation of the model's performance over time. Additionally, some researchers use cross-validation techniques, such as k-fold cross-validation, to further validate the model's performance. These approaches help ensure that the model is trained and tested on diverse and representative data, leading to more accurate results in cryptocurrency analysis.
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• Dec 27, 2021 · 3 years ago
  Selecting train-test splits and random states in cryptocurrency analysis requires careful consideration. One approach is to use a stratified sampling method, which ensures that the training and testing sets have a similar distribution of data. This can be particularly useful when dealing with imbalanced datasets, where certain classes or categories are underrepresented. Another technique is to use a holdout validation set, where a portion of the data is set aside for validation purposes. This allows for the evaluation of the model's performance on unseen data. Additionally, it's important to consider the random state used in the splitting process. By setting a specific random state, the results can be reproduced, which is crucial for research and analysis. Overall, the recommended approaches for selecting train-test splits and random states in cryptocurrency analysis depend on the specific dataset and research goals, but these techniques can help ensure the validity and reliability of the results.
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• Dec 27, 2021 · 3 years ago
  In cryptocurrency analysis, selecting train-test splits and random states is an important step in building accurate models. At BYDFi, we recommend using a combination of random sampling and stratified sampling techniques. Random sampling ensures that the data is divided randomly, reducing the risk of bias. Stratified sampling, on the other hand, ensures that the training and testing sets have a similar distribution of data, which is particularly useful when dealing with imbalanced datasets. Additionally, it's important to consider the random state used in the splitting process. This allows for the reproducibility of results and ensures that the model can be tested on the same data multiple times. By following these recommended approaches, researchers and analysts can build robust models for cryptocurrency analysis.
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