Package ml-experiment
Introduction
This Python package facilitates the fast prototyping of machine learning model with great scalability and flexibility.
Characteristics of this package:
- Flexibility of Feature Engineering: it is convenient to define a function to put to feature-processing pipeline;
- Flexibility of Models: there is no restriction about whether you have to use scikit-learn, TensorFlow, or PyTorch;
- Few Specifications on Models: user only need to worry about the
fitandpredict_proba; - Training Job Specifications: features, data locations, model specifications can be specified in a Python dictionary or JSON, facilitating potential MapReduce or parallelism;
- Scalability: data is stored temporarily in disks in batch to save memory space;
- Statistics: statistical measures of the performance of the models and their class labels are calculated;
- Cross Validation: cross validation option is available.
- Ready Adaptation to Production: data pipelines and algorithms can be adapted into production codes with little changes.
There will be tutorials and documentations.
News
- 06/24/2020:
0.0.6released. - 05/31/2020:
0.0.5released. - 05/12/2020:
0.0.4released. - 05/03/2020:
0.0.3released. - 04/29/2020:
0.0.2released. - 04/24/2020:
0.0.1released.
