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 – u) / sminmax 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.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.