AutoOptimizer is a python package for optimize machine learning algorithms.
Project description
AutoOptimizer provides tools to automatically optimize machine learning model for every dataset.
It refers to techniques that allow semi-sophisticated machine learning practitioners and non-experts to discover a good predictive model pipeline for their machine learning algorithm task quickly with very little intervention other than providing a dataset.
#Prerequisites:
-sklearn
-numpy
-pandas
#install package:
pip install autooptimzer
#install package in jupyter notebook:
1-open anaconda prompt (recommended open as administrator)
2-pip install autooptimzer
#Usage:
Optimize scikit learn supervised, unsupervised and ensemble learning models using python.
{DBSCAN, KMeans, MeanShift, LogisticRegression, LinearRegression, KNeighborsClassifier, KNeighborsRegressor, DecisionTreeClassifier, DecisionTreeRegressor RandomForestClassifier, RandomForestRegressor, GradientBoostingClassifier, GradientBoostingRegressor, AdaBoostClassifier, AdaBoostRegressor, SupportVectorClassifier, BaggingClassifier, BaggingRegressor, ExtraTreesClassifier }
Metrics for Your Regression Model
Clear data by removing outliers
for more information visit: http://genesiscube.ir/index.php/autooptimizer/
#Contact and Contributing: Please share your good ideas with us. Simply letting us know how we can improve the programm to serve you better. Thanks for contributing with the program.
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