Metadata-Version: 2.1
Name: model-performance-investigator
Version: 1.0.1
Summary: Package give idea of a various models based on given data
Home-page: https://github.com/srikandan/model-evaluation
Author: Srikandan
Author-email: kandan.sri15@gmail.com
License: UNKNOWN
Description: # Model Performance Investigator
        
        ## Short Description
                Model Performance Investigator is used to analyse the performance Machine Learning and
            Deep learning models. It gives the user basic idea how the model perfomance on given data.
            So that user can start work on the right model.
            
        ## Installation
            pip install model-performance-analyzer
            
        ## How to use ?
            
            * Import the package.
                from model_analyzer import ml_models as ml
            
            * Call the predector function, assign it to a variable and run.
                data = [train_X, train_y, text_x]
                new_out = ml.predector('regression', data, alg_type=['linear','lasso'], 
                          score_type=['r2'], tune_param='default')
        
        ## Parameters ?
            1. 'regression' -> Current Version uses only regression type problems. It is mandatory.
            
            2. data -> data must contain "train_X, train_y, text_x". train_X, train_y is for fitting
                       the model and test_x is for predicting. It is mandatory.
                       
            3. alg_type -> Type of the algorithm. It is optional parameter. Provide the parameter
                           values in list.  
                           Default is "Linear Regression" algorithm.
                           
                           Currently it Supports:
                           
                            Algorithm Name             | Parameter 
                            ---------------------------|----------
                            Linear Regression        | linear
                            Polynomial Regression    | polynomial
                            Redig Regression         | ridge
                            Lasso Regression         | lasso
                            ElasticNet Regression    | elasticnet
                            Random Forest Regression | random_forest_regressor
                            XG Boost Regressor       | xgb_regressor
                            
            4. score_type -> Type of the score. It is optional parameter. Provide the parameter
                             values in list.   
                             Default is "r2" algorithm.
                           
                           Currently it Supports:
                           * r2
                           * explained_variance
                           * max_error
                           * neg_mean_absolute_error
                           * neg_mean_squared_error
                           * neg_mean_squared_log_error
                           * neg_median_absolute_error
                           * neg_mean_poisson_deviance
                           * neg_mean_gamma_deviance
                    
            5. tune_param -> Tuning Parameter for the model. It is optional. By Default parameter 
                             this package uses some basic parameters for each models.
                             User can provide own Tuning Parameter.
                             
                             Template:
                                 tune_param = {Name of the model: {parameters}}
                                 Name of the model -> should be similar like alg_type.
                                 
                             Example:
                                 linear_tune_param = {
                                      "fit_intercept"       : [True, False],
                                      "normalize"           : [True, False],
                                      "copy_X"              : [True, False],
                                      "n_jobs"              : [ -1]
                                    }
                                lasso_tune_param = {
                                      "alpha":[1e-15, 1e-10, 1e-8, 1e-3, 1e-2, 
                                               1, 5, 10, 15, 20, 40, 50,
                                               85, 100, 300, 500, 1000
                                               ]
                                    }
                                    
                                 tune_param = {'linear': linear_tune_param, 
                                              'lasso': lasso_tune_param}
                             
                             Note : User don't have to specify tuning parameter for all the
                                    models used in 'alg_type'. If tuning parameter is not provided
                                    then this package use default tuning parameter.
                                    
        ## Project History
           The project was started in 2019 by Srikandan Rajua and Sathish Anandha.
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
