SNM Algorithm

[HebaBelal]

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Hey,

Seeking your support to clarify the variance between the below Commands:

SVM = SVC(kernel='linear',random_state=0)
SVM = SVC(kernel='linear')

Thanks in advance.

[queenofcorgis]

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[HebaBelal]

Thanks

[mislam]

The difference between these two commands lies in the random_state parameter:

SVM = SVC(kernel='linear', random_state=0)
SVM = SVC(kernel='linear')

Key Differences:

1. With random_state=0:

• This sets a fixed seed for the internal random number generator used by SVC.
• Ensures reproducibility: Running the model multiple times on the same data will always yield the same results.

2. Without random_state:

• The default behavior is used, meaning the randomness in the algorithm (e.g., data shuffling, breaking ties) can lead to slightly different results each time you run the model.
• Useful if you don’t need strict reproducibility and want some variability in model performance.

When to Use random_state?

• If you are running experiments and want consistent results, set random_state=0 (or any fixed number).
• If you don’t set random_state, results may change slightly due to internal randomness.

Would you like more details on how random_state impacts Support Vector Classification (SVC)?

Hope this clarifies your question! If you found this helpful, please Mark as answer. 😊

[HebaBelal]

@mislam Appreciated