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  • 0 - 1 Normalization
  • Z-score Normalization

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  1. ML & Data Science
  2. ML Techniques

Feature Normalization

PreviousSelection functionsNextCross-validation

Last updated 1 month ago

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0 - 1 Normalization

xj[i]=xj[i]−min(xj)max(xj)−min(xj)x_j^{[i]} = \frac{x_j^{[i]} - min(x_j) }{max(x_j) - min(x_j)} xj[i]​=max(xj​)−min(xj​)xj[i]​−min(xj​)​

Z-score Normalization

More recommended when using DL methods (due to the zero-centering)

xj[i]=xj[i]−mean(xj)std(xj)x_j^{[i]} = \frac{x_j^{[i]} - mean(x_j) }{std(x_j)} xj[i]​=std(xj​)xj[i]​−mean(xj​)​