Transformers offer a basic building block to executing machine learning pipelines. A transformer takes data from a data frame, transforms it using some operation and outputs one or more new fields back to the data frame. This is a very simple task, but is at the core of the Spark, Scikit-learn, MLeap and Tensorflow execution engines.

Transformers are used for many different tasks, but the most common in machine learning are:

  1. Feature extraction
  2. Model scoring

Feature Extraction

Feature extraction is the process of taking one of more features from and input dataset and deriving new features from them. In the case of data frames, the features come from the input data frame and are written to the output data frame.

Some examples of feature extraction are:

  1. String indexing (label encoding) - Taking a string and converting it to an integer value
  2. One hot encoding - Converting an integer value to a vector of 1s and 0s
  3. Feature selection - Running analysis to determine which features are most effective for training a predictive ML algorithm (i.e. CHI2)
  4. Math - Basic mathematical functions, such as dividing two features by each other or taking the log of a feature

There are too many examples of feature extraction to enumerate here, but take a look at our complete list of supported feature transformers to get an idea of what is possible.


Regression is used to predict a continuous numeric value, such as the price of a car or a home. Regression models, for the most part, operate on vectors of doubles called a "feature vector". The feature vector contains all of the known information about what is being predicted. In the case of predicting a price of a house, the feature vector will have things like the encoded region where the house is, the square footage, how many bathrooms there are, how old it is, etc.

See a list of supported regression models.


Classification is used to predict categorical information. An example is making a binary prediction of whether or not to give a consumer a loan. Another example is predicting what type of sound is contained in a .wav file, or whether or not there is a person in and image.

Supported classification models.


Clustering is used to assign a label to similar data (thus categorizing/clustering it). It is similar to classification in that the predictions are discrete values from a set. Unlike classification models though, clustering models are part of the unsupervised family of models and do not operate on labeled data. This is useful for feature engineering, anomaly detection, as well as many other tasks.

Supported clustering models.

Other Types of Transformers

Transformers can do ANYTHING! This is just a sample of the most common uses of them. However, you can build transformers to resize images, resample sound data, import data from different data sources or anything else you can think of. The sky is the limit.

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