pipe_short has steps that were automatically named we can see the names of the steps by looking at the steps attributein[ ]print("pipeline steps:\ {}format(pipe_short steps)out[ ]pipeline steps[('minmaxscaler'minmaxscaler(copy=truefeature_range=( )))('svc'svc( = cache_size= class_weight=nonecoef = decision_function_shape=nonedegree= gamma='auto'kernel='rbf'max_iter=- probability=falserandom_state=noneshrinking=truetol= verbose=false))the steps are named minmaxscaler and svc in generalthe step names are just lowercase versions of the class names if multiple steps have the same classa number is appendedin[ ]from sklearn preprocessing import standardscaler from sklearn decomposition import pca pipe make_pipeline(standardscaler()pca(n_components= )standardscaler()print("pipeline steps:\ {}format(pipe steps)out[ ]pipeline steps[('standardscaler- 'standardscaler(copy=truewith_mean=truewith_std=true))('pca'pca(copy=trueiterated_power= n_components= random_state=nonesvd_solver='auto'tol= whiten=false))('standardscaler- 'standardscaler(copy=truewith_mean=truewith_std=true))as you can seethe first standardscaler step was named standardscaler- and the second standardscaler- howeverin such settings it might be better to use the pipeline construction with explicit namesto give more semantic names to each step accessing step attributes often you will want to inspect attributes of one of the steps of the pipeline--saythe coefficients of linear model or the components extracted by pca the easiest way to access the steps in pipeline is via the named_steps attributewhich is dictionary from the step names to the estimators algorithm chains and pipelines