transformerstransformation of input data before learning of model is very common task in machine learning some data transformations are simplefor example replacing some missing data with constanttaking log transformwhile some data transformations are similar to learning algorithms themselves (for examplepcato simplify the task of such transformationssome estimator objects will implement the transformer interface this interface allows us to perform non-trivial transformation on the input data and supply the output to our actual learning algorithm since the transformer object will retain the estimator used for transformationit becomes very easy to apply the same transformation to unknown test data using the transform function advanced apis in the earlier section we saw some of the most basic tenets of the scikit-learn package in this section we will briefly touch on the advanced constructs that are built on those basics these advanced set of apis will often help data scientists in expressing complex set of essential operations using simple and stream-lined syntax meta estimatorsthe meta estimator interface (implemented using the multiclass interfaceis collection of estimators which can be composed by accumulating simple binary classifiers it allows us to extend the binary classifiers to implement multi-classmulti-labelmulti-regressionand multi-class-multi-label classifications this interface is important as these scenarios are common in modern day machine learning and the capability to implement this out-of-the-box reduces the programming requirements for data scientists we should also remember that most binary estimators in the scikit-learn library have multiclass capabilities built in and we won' be using the meta-estimators unless we need custom behavior pipeline and feature unionsthe steps of machine learning are mostly sequential in nature we will read in the dataapply some simple or complex transformationsfit an appropriate modeland predict using the model for unseen data another hallmark of the machine learning process is the iteration of these steps multiple times due to its iterative natureto arrive at the best possible model and then deploy the same it is convenient to chain these operations together and repeat them as single unit instead of applying operations piecemeal this concept is also known as machine learning pipelines scikit-learn provides pipeline api to achieve similar purpose pipeline(object from the pipeline module can chain multiple estimators together (transformationsmodelingetc and the resultant object can be used as an estimator itself in addition to the pipeline apiwhich applies these estimators in sequential methodwe also have access to featureunion apiwhich will perform specified set of operation in parallel and show the output of all the parallel operations the use of pipelines is fairly advanced topic and it will be made clearerwhen we specifically see an example in the subsequent