Package give idea of a models performance based on given data
Project 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 performance 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 supports Regression and Classification
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.
1. Regression:
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
2. Classification:
It Supports:
* jaccard
* f1
* neg_log_loss
* roc_auc
* accuracy
* balanced_accuracy
* average_precision
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.
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