zscore
.True
, the feature space is transformed using the method defined under the normalized_method
parameter. zscore
. The other available options are:z-score
The standard zscore is calculated as z = (x โ u) / sminmax
scales and translates each feature individually such that it is in the range of 0 โ 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.yeo-johnson
and quantile
.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.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.box-cox
and yeo-johnson
.True
, the target variable is transformed using the method defined in transform_target_method
parameter. Target transformation is applied separately from feature transformations.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.pycaret.classification module.