np.random.seed(42) initializes the random number generator with a specific seed value (in this case, 42). The seed determines the starting point for the sequence of random numbers.
Here's how it works:
Random Number Generation is Deterministic: Computers don't generate truly random numbers. Instead, they use algorithms called pseudo-random number generators (PRNGs). These algorithms produce sequences of numbers that appear random but are actually completely determined by an initial value called the "seed."
Setting the Seed: When you set the seed with np.random.seed(42), you're telling the PRNG to start its sequence from a specific point. If you use the same seed every time you run the code, the PRNG will produce the exact same sequence of numbers.
Reproducibility: This is why it makes the code reproducible. If you (and anyone else) run the code with np.random.seed(42) at the beginning, you'll get the same "random" numbers. This is crucial for:
Debugging: If you get an unexpected result when your code involves randomness, you can set the seed to a specific value to reproduce the exact same behavior and debug the issue.
Scientific Research: In machine learning and other scientific fields, it's essential to be able to reproduce experiments. Setting the random seed ensures that the random parts of the experiment (e.g., initializing weights, shuffling data) are the same each time.
Consistent Behavior: For applications where you want the "randomness" to be the same across different runs (e.g., generating the same random test data), setting the seed is essential.
In the context of the provided code, np.random.seed(42) ensures that the generated sample data with outliers is the same every time the code is run. This is important for consistency when testing or demonstrating the effects of outliers on box plots and histograms. If the seed wasn't set, the generated data would be different each time, and the resulting plots would also vary.
No comments:
Post a Comment