ENH Rework narrative of GBDT notebook #763
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glemaitre
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Apr 26, 2024
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| # %% | ||
| import pandas as pd | ||
| import numpy as np | ||
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| # Create a random number generator that will be used to set the randomness | ||
| rng = np.random.RandomState(0) | ||
| rng = np.random.RandomState(0) # Create a random number generator |
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Let's move the generator next to the data generation. We should avoid showing a pattern where people would use the generator across different function and estimators.
So the best practice is just to show it next to the data generation. we could even slightly change the code and have:
def generate_data(n_samples=50, seed=0):
rng = np.random.default_rng(seed)
x = rng.normal(size=(n_samples,)) * ...
noise = rng.normal(size=(s_samples,)) * 0.3using default_rng should be encourage nowadays.
Co-authored-by: Guillaume Lemaitre <g.lemaitre58@gmail.com>
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Co-authored-by: ArturoAmorQ <arturo.amor-quiroz@polytechnique.edu> Co-authored-by: Guillaume Lemaitre <g.lemaitre58@gmail.com> 31bfaaf
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Originally I wanted to rework only the wording, as well explaining the GBDT algo is the cornerstone for understanding HGBT. But then I decided to factorize the code by introducing a helper function to keep the focus on the narrative other than the code.