machine_learning.data_transformations

Normalization.

Wikipedia: https://en.wikipedia.org/wiki/Normalization Normalization is the process of converting numerical data to a standard range of values. This range is typically between [0, 1] or [-1, 1]. The equation for normalization is x_norm = (x - x_min)/(x_max - x_min) where x_norm is the normalized value, x is the value, x_min is the minimum value within the column or list of data, and x_max is the maximum value within the column or list of data. Normalization is used to speed up the training of data and put all of the data on a similar scale. This is useful because variance in the range of values of a dataset can heavily impact optimization (particularly Gradient Descent).

Standardization Wikipedia: https://en.wikipedia.org/wiki/Standardization Standardization is the process of converting numerical data to a normally distributed range of values. This range will have a mean of 0 and standard deviation of 1. This is also known as z-score normalization. The equation for standardization is x_std = (x - mu)/(sigma) where mu is the mean of the column or list of values and sigma is the standard deviation of the column or list of values.

Choosing between Normalization & Standardization is more of an art of a science, but it is often recommended to run experiments with both to see which performs better. Additionally, a few rules of thumb are:

1. gaussian (normal) distributions work better with standardization

2. non-gaussian (non-normal) distributions work better with normalization

3. If a column or list of values has extreme values / outliers, use standardization

Functions

normalization(→ list)	Return a normalized list of values.
standardization(→ list)	Return a standardized list of values.

Module Contents

machine_learning.data_transformations.normalization(data: list, ndigits: int = 3) → list

Return a normalized list of values.

@params: data, a list of values to normalize @returns: a list of normalized values (rounded to ndigits decimal places) @examples: >>> normalization([2, 7, 10, 20, 30, 50]) [0.0, 0.104, 0.167, 0.375, 0.583, 1.0] >>> normalization([5, 10, 15, 20, 25]) [0.0, 0.25, 0.5, 0.75, 1.0]

machine_learning.data_transformations.standardization(data: list, ndigits: int = 3) → list

Return a standardized list of values.

@params: data, a list of values to standardize @returns: a list of standardized values (rounded to ndigits decimal places) @examples: >>> standardization([2, 7, 10, 20, 30, 50]) [-0.999, -0.719, -0.551, 0.009, 0.57, 1.69] >>> standardization([5, 10, 15, 20, 25]) [-1.265, -0.632, 0.0, 0.632, 1.265]