skerrorlearner 0.1.50

Skerrorlearner is an Error Learning Package for Machine Learning

skerrorlearner is an Error Learning Package for Machine Learning use cases. It is available for both Regression and Classification problems under Supervised Machine Learning. This helps build models that learn the error of the current model being built. This approach is taken towards Machine Learning Model Performance Improvement.

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Documentation

You can read the documentation here.

License

Apache License 2.0

Installation

Using pip

You can use pip to install skerrorlearner. Copy the below command and paste in Command Prompt to install skerrorlearner.

pip install skerrorlearner

To upgrade the package, copy the below command and paste in Command Prompt to upgrade skerrorlearner.

pip install skerrorlearner --upgrade

Usage

As we highly believe in hands-on rather than reading documentations, we have got usage guides in the form of .ipynb notebooks. Below are the linked usage guides.

• Regression Use Case

• Classification Use Case

Further, if you fork the Skerrorlearner Use Case Demo directory, you’ll be able to get the data on top of which skerrorlearner was tested. You’ll also be able to get the .ipynb notebook to understand how the library works.

Once you have forked the library, we’d highly recommend you to read the dockstring of each method falling under skerrorlearner package to know what parameters are to be passed and what is the use of the method.

Support & Advantages

The library supports below algorithms to build Error Models.

Regression Use Case

Scikit Learn	Non-Scikit Learn
Linear Regression	XGBoost
Support Vector Machine	LightGBM
Decision Tree
Random Forest
K-Nearest Neighbors
AdaBoost
GradientBoost

Classification Use Case

Scikit Learn	Non-Scikit Learn
Logistic Regression	XGBoost
Support Vector Machine	LightGBM
Decision Tree	CatBoost
Random Forest
K-Nearest Neighbors
AdaBoost
GradientBoost
GaussianNB

Advantages

• Supports Hackathon Data Prediction

• Supports Production Live Data Prediction