Common machine learning tools and training models can be used to solve common regression and classification problems. The model includes the most commonly used XGBoost and LightGBT models, as well as the basic linear model and SVM that will be gradually improved in the future, and the DNN model based on PyTorch will be implemented. At the same time, feature processing and analysis will be gradually added, as well as comprehensive model index evaluation capabilities.
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Release files for mlkits 0.0.6
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Built distributions (wheels)
| File | Interpreter | ABI | Platform | Actions Reset |
|---|---|---|---|---|
| mlkits-0.0.6-py3.8.egg | Legacy Egg format | - | - | Details for mlkits-0.0.6-py3.8.egg |
| mlkits-0.0.6-py3-none-any.whl | Python 3 | none | any | Details for mlkits-0.0.6-py3-none-any.whl |
Total release size: 102.9 kB
Release files / mlkits-0.0.6-py3.8.egg
| Download URL | mlkits-0.0.6-py3.8.egg |
|---|---|
| Size | 72.2 kB |
| Tags | Egg |
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SHA-256 checksum How to use checksums |
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twine/4.0.2 CPython/3.8.13
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Release files / mlkits-0.0.6-py3-none-any.whl
| Download URL | mlkits-0.0.6-py3-none-any.whl |
|---|---|
| Size | 30.7 kB |
| Tags | Python 3 |
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SHA-256 checksum How to use checksums |
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No |
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twine/4.0.2 CPython/3.8.13
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