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ML Techniques
Feature Normalization
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0 - 1 Normalization
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x_j^{[i]} = \frac{x_j^{[i]} - min(x_j) }{max(x_j) - min(x_j)}
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Z-score Normalization
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More recommended when using DL methods (due to the zero-centering)
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x_j^{[i]} = \frac{x_j^{[i]} - mean(x_j) }{std(x_j)}
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Last updated
1 year ago
0 - 1 Normalization
Z-score Normalization
