#About The Auto Optimizer Package

Machine Learning algorithm optimizer for sklearn and evaluation Metrics for Regression Model.
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(contains all sub packages except mlxtend) or:
	sklearn
	matplotlib
	mlxtend
	numpy

#Installation
	Github:
		https://github.com/mrb987/autooptimizer.git
	Install package:
		pip install autooptimizer

#Usage
scikit learn supervised and unsupervised learning models using python.
{DBSCAN, KMeans, MeanShift,  LogisticRegression, KNeighborsClassifier, SupportVectorClassifier, DecisionTree}

#Running:
>>from autooptimizer.dbscan import dbscan
>>from autooptimizer.kmeans import kmeans
>>from autooptimizer.meanshift import meanshift
>>from autooptimizer.logreg import logreg
>>from autooptimizer.knn import knn
>>from autooptimizer.svc import svc
>>from autooptimizer.decisiontree import decisiontree
>>dbscan(x)
>>kmeans(x)
>>meanshift(x)
>>logreg(x,y)
>>knn(x,y)
>>svc(x,y)
>>decisiontree(x,y)

'x' should be your independent variable or feature's values and 'y' is target variable or dependent variable.
The output of the program is the maximum possible accuracy with the appropriate parameters to use in model.

#Metrics
>>from autooptimizer.metrics import root_mean_squared_error
>>from autooptimizer.metrics import root_mean_squared_log_error
>>from autooptimizer.metrics import root_mean_squared_precentage_error
>>from autooptimizer.metrics import symmetric_mean_absolute_precentage_error
>>from autooptimizer.metrics import mean_bias_error
>>from autooptimizer.metrics import relative_squared_error
>>from autooptimizer.metrics import root_relative_squared_error
>>from autooptimizer.metrics import relative_absolute_error
>>from autooptimizer.metrics import median_absolute_percentage_error
>>from autooptimizer.metrics import mean_absolute_percentage_error
>>
>>root_mean_squared_error(true, predicted)
>>root_mean_squared_log_error(true, predicted)
>>root_mean_squared_precentage_error(true, predicted)
>>symmetric_mean_absolute_precentage_error(true, predicted)
>>mean_bias_error(true, predicted)
>>relative_squared_error(true, predicted)
>>root_relative_squared_error(true, predicted)
>>relative_absolute_error(true, predicted)
>>median_absolute_percentage_error(true, predicted)
>>mean_absolute_percentage_error(true, predicted)

#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 programm.

>>>https://github.com/mrb987/autooptimizer
>>>info@genesiscube.ir