Dynamic-Learning-Technique 0.3

Dynamic learning technique allows the user to train a model in batch wise manner

Dynamic Learning Technique

Dynamic learning technique allows the user to train a model in batch wise manner

Installation

Use the package manager pip to install Exchange Rate Api

pip install Dynamic-Learning-Technique

Usage

The DLT takes 2 argument with 6 optional arguments

# Initialize an object to DLT
from DLT import *
from sklearn.tree import DecisionTreeRegressor
import asyncio


async def main():
    obj = DLT(['X dataset'], ['Y dataset'], DecisionTreeRegressor())
    await obj.start()


if __name__ == "__main__":
    asyncio.run(main())

Features

• Algorithms Supported

  New supported algorithms has been included

  • RandomForestClassifier
  • DecisionTreeClassifier
  • SVC
  • RandomForestRegressor
  • DecisionTreeRegressor
  • LinearRegression
  • LogisticRegression
  • SVR
  • Ridge
  • Lasso

• Exception

  New exceptions has been included

  • NoArgumentException
  • InvalidMachineLearningModel
  • InvalidDatasetProvided
  • BatchCountGreaterThanBatchSize

• Parallel Processing

  • The splitting process has been made an asynchronous process in order to increase the speed of the splitting process

Test cases has been included

Contributing

Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

Please make sure to update tests as appropriate.

License

MIT