Answer-first summary for fast verification
Answer: z-test: A statistical method used to determine whether two population means are different when the variances are known and the sample size is large.
The z-test is a statistical hypothesis test that compares the means of two samples to determine if they are significantly different from each other. It is not a data normalization technique. Correct normalization methods include Feature Clipping (A), which caps the values of features to a specified range; z-score (B), which standardizes data to have a mean of 0 and a standard deviation of 1; Scaling to a range (C), which adjusts values to fit within a specified range; and log scaling (D), which transforms data using logarithms to alter distribution shape while maintaining order. For more details on data normalization techniques, refer to Google's Machine Learning Guide.
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You are tasked with optimizing a deep neural network model in TensorFlow that processes large numerical datasets. Given the constraints of not being able to allocate additional resources and the urgency to meet project deadlines, your mentor suggests implementing data normalization techniques to enhance model performance. Data normalization is crucial for improving the speed and stability of the training process. Considering the following techniques, which one is NOT a method of data normalization? Please choose one option.
A
Feature Clipping: Limits the values of features to a specified range, effectively capping outliers.
B
z-score: Standardizes the data to have a mean of 0 and a standard deviation of 1.
C
Scaling to a range: Adjusts the feature values to fit within a specified range, such as [0, 1] or [-1, 1].
D
log scaling: Applies a logarithmic transformation to the data to alter the distribution shape while preserving the order of magnitudes.
E
z-test: A statistical method used to determine whether two population means are different when the variances are known and the sample size is large.