Scale and Transform

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Scale and Transform
Normalize
Normalization is a technique often applied as part of data preparation for machine learning. The goal of normalization is to rescale the values of numeric columns in the dataset without distorting differences in the ranges of values or losing information. There are several methods available for normalization, by default, PyCaret uses zscore.
PARAMETERS
normalize: bool, default = False
When set to True, the feature space is transformed using the method defined under the normalized_method parameter. 
normalize_method: string, default = ‘zscore’
Defines the method to be used for normalization. By default, the method is set to zscore. The other available options are:
z-score The standard zscore is calculated as z = (x – u) / s
minmax scales and translates each feature individually such that it is in the range of 0 – 1.
maxabs scales and translates each feature individually such that the maximal absolute value of each feature will be 1.0. It does not shift/center the data and thus does not destroy any sparsity.
robust scales and translates each feature according to the Interquartile range. When the dataset contains outliers, the robust scaler often gives better results.
Example
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# load dataset
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from pycaret.datasets import get_data
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pokemon = get_data('pokemon')
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​
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# init setup
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from pycaret.classification import *
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clf1 = setup(data = pokemon, target = 'Legendary', normalize = True)
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Before
After
Effect of Normalization:
Feature Transform
While normalization rescales the data within new limits to reduce the impact of magnitude in the variance, Feature transformation is a more radical technique. Transformation changes the shape of the distribution such that the transformed data can be represented by a normal or approximate normal distribution. There are two methods available for transformation yeo-johnson and quantile.
PARAMETERS
transformation: bool, default = False
When set to True, a power transformer is applied to make the data more normal / Gaussian-like. This is useful for modeling issues related to heteroscedasticity or other situations where normality is desired. The optimal parameter for stabilizing variance and minimizing skewness is estimated through maximum likelihood.
transformation_method: string, default = ‘yeo-johnson’
Defines the method for transformation. By default, the transformation method is set to yeo-johnson. The other available option is quantile transformation. Both the transformation transforms the feature set to follow a Gaussian-like or normal distribution. Quantile transformer is non-linear and may distort linear correlations between variables measured at the same scale.
Example
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# load dataset
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from pycaret.datasets import get_data
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pokemon = get_data('pokemon')
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​
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# init setup
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from pycaret.classification import *
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clf1 = setup(data = pokemon, target = 'Legendary', transformation = True)
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Before
Dataframe view before transformation
After
Dataframe view after transformation
Effect of Feature Transformation:
Target Transform
Target Transformation is similar to Feature Transformation as it will change the shape of the distribution of the target variable instead of Features. There are two methods supported for the target transformation box-cox and yeo-johnson.
PARAMETERS
transform_target: bool, default = False
When set to True, the target variable is transformed using the method defined in transform_target_method parameter. Target transformation is applied separately from feature transformations.
transform_target_method: string, default = ‘box-cox’
box-cox requires input data to be strictly positive, while yeo-johnson supports both positive and negative data. When transform_target_method is box-cox and target variable contains negative values, the method is internally forced to yeo-johnson to avoid any exceptions.
Example
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# load dataset
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from pycaret.datasets import get_data
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diamond = get_data('diamond')
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​
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# init setup
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from pycaret.regression import *
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reg1 = setup(data = diamond, target = 'Price', transform_target = True)
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Before
Dataframe view before target transformation
After
Dataframe view after target transformationn
NOTE: This functionality is only available in pycaret.classification module.
Data Preparation
Feature Engineering
Last modified 3mo ago
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Contents
Normalize
Feature Transform
Target Transform