Linear Learner Algorithm
Linear Learner Algorithm is a Supervised Learning algorithm that can be used to solve three types of problems: Binary classification; Multi-class classification; and Regression. The algorithm is trained with lists of data comprising a high dimensional vector x and a label y to learn the equation of the line.
The Linear Learner Algorithm uses Stochastic Gradient Descent to fit a line to the data points.
- AWS docs: https://docs.aws.amazon.com/sagemaker/latest/dg/linear-learner.html
- https://towardsdatascience.com/using-aws-sagemakers-linear-learner-to-solve-regression-problems-36732d802ba6
Attributes
| Problem attribute | Description |
| Data types and format | Tabular |
| Learning paradigm or domain | Supervised Learning |
| Problem type | Binary/multi-class classification, Regression |
| Use case examples | Predict a numeric/continuous value; Predict if an item belongs to a category |
Training
Input data
| X | Y | |
| High dimensional vector | Numeric label | |
| Binary classification | 0 or 1 | |
| Multi-class classification | 0 to num_class – 1 | |
| Regression | Real numbers |
Format:
- recordIO-wrapped protobuf
- CSV
Model artifacts and inference
| Description | Artifacts |
| Learning paradigm | Supervised Learning |
| Request format | recordIO-wrapped protobuf, CSV, JSON |
| Result | Regression: predicted score Binary Classification: predicted label 0 or 1 and the strength of the prediction. Multi-class Classification: predicted label and a list of scores for each class showing the strength of the prediction. |
Processing environment
For training the Linear Learner Algorithm single or multi-machine CPU and GPU instances can be used. The same can be used for inferencing with GPU providing little or no improvement over CPU instances.
Video
Amazon SageMaker’s Built-in Algorithm Webinar Series: Linear Learner
This is a 58.54 minutes video from AWS.
Credits
Pegs on clothes line photo by Kyle Arcilla on Unsplash
