AWS SageMaker Algorithms

Linear Learner

Linear Learner is a supervised learning algorithm that is used to fit a line to the training data.

It could be used for both classification and regression tasks as follows:

  • Regression: output contains continuous numeric values
  • Binary classification: output label must be either 0 or 1 (linear threshold function is used)
  • Multiclass classification: output labels must be from 0 to num_classes - 1

The best model optimizes either of the following:

  • For regression: focus on Continuous metrics such as mean square error, root mean squared error, cross entropy loss, absolute error
  • For classification: focus on discrete metrics such as F1 score, precision, recall or accuracy.


  • Ensure that data is shuffled before training
  • Normalization or feature scaling is offered by Linear Learner (positive)
  • Normalization or feature scaling is a critical pre-processing step to ensure that the model does not become dominated by the weight of a single feature


  • Linear Learner uses stochastic gradient descent to perform the training
  • Select an appropriate optimization algorithm such as Adam, AdaGrad, and SGD
  • Hyper-parameters, such as learning rate can be selected
  • Overcome model over-fitting using L1, L2 regularization


Trained models are evaluated against a validation dataset and best model is selected based on the following metrics:

  • For regression: mean square error, root mean squared error, cross entropy loss, absolute error.
  • For classification: F1 score, precision, recall, or accuracy.

Linear Learner Input/Output data

Amazon SageMaker linear learner supports the following input data types:

  • RecordIO-wrapped protobuf (only Float32 tensors are supported)
  • Text/CSV (note: First column assumed to be the target label)
  • File or Pipe mode both supported

For inference, linear learner algorithm supports the application/json, application/x-recordio-protobuf, and text/csv formats.

For regression (predictor_type=‘regressor’), the score is the prediction produced by the model.