Dynamic learning technique allows the user to train a model in batch wise manner
Project description
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
DLT(['X dataset'], ['Y dataset'], DecisionTreeRegressor())
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
Project details
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