AutoOptimizer is a python package for optimize ML algorithms.
Project description
AutoOptimizer provides tools to automatically optimize machine learning model for a dataset with very little user intervention.
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:
jupyterlab or: {sklearn, matplotlib, numpy}
#Usage:
Optimize scikit learn supervised, unsupervised and ensemble learning models using python.
{DBSCAN, KMeans, MeanShift, LogisticRegression, LinearRegression, KNeighborsClassifier, KNeighborsRegressor, RandomForestClassifier, GradientBoostingClassifier, AdaBoostClassifier, SupportVectorClassifier, DecisionTree}
Metrics for Your Regression Model
Clear data by removing outliers
for more information visit: http://genesiscube.ir/index-6.html
#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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