A python package that does model comparison between different regression models.
pip install git+https://github.com/UBC-MDS/RegscorePy.gitAIC stands for Akaike’s Information Criterion. It estimates the quality of a model, relative to each of other models. The lower AIC score is, the better the model is. Therefore, a model with lowest AIC - in comparison to others, is chosen.
AIC = n*log(residual sum of squares/n) + 2K
where:
- n: number of observations
- K: number of parameters (including intercept)
aic(y, y_pred, p)
Parameters:
-
y: array-like of shape = (n_samples) or (n_samples, n_outputs)
- True target variable(s)
-
y_pred: array-like of shape = (n_samples) or (n_samples, n_outputs)
- Fitted target variable(s) obtained from your regression model
-
p: int
- Number of predictive variable(s) used in the model
Return:
- aic_score: int
- AIC score of the model
BIC stands for Bayesian Information Criterion. Like AIC, it also estimates the quality of a model. When fitting models, it is possible to increase model fitness by adding more parameters. Doing this may result in model overfit. Both AIC and BIC help to resolve this problem by using a penalty term for the number of parameters in the model. This term is bigger in BIC than in AIC.
BIC = n*log(residual sum of squares/n) + K*log(n)
where:
- n: number of observations
- K: number of parameters (including intercept)
bic(y, y_pred, p)
Parameters:
-
y: array-like of shape = (n_samples) or (n_samples, n_outputs)
- True target variable(s)
-
y_pred: array-like of shape = (n_samples) or (n_samples, n_outputs)
- Fitted target variable(s) obtained from your regression model
-
p: int
- Number of predictive variable(s) used in the model
Return:
- bic_score: int
- BIC score of the model
Mallow's C_p is named for Colin Lingwood Mallows. It is used to assess the fit of regression model, finding the best model involving a subset of predictive variables available for predicting some outcome.
C_p = (SSE_p/MSE) - (n - 2p)
where:
- SSE_k: residual sum of squares for the subset model containing
pexplanatory variables counting the intercept. - MSE: mean squared error for the full model (model containing all
kexplanatory variables of interest) - n: number of observations
- p: number of subset explanatory variables
mallow(y, y_pred, y_sub, k, p)
Parameters:
-
y: array-like of shape = (n_samples) or (n_samples, n_outputs)
- True target variable(s)
-
y_pred: array-like of shape = (n_samples) or (n_samples, n_outputs)
- Fitted target variable(s) obtained from your regression model
-
y_sub: array-like of shape = (n_samples) or (n_samples, n_outputs)
- Fitted target variable(s) obtained from your subset regression model
-
k: int
- Number of predictive variable(s) used in the model
-
p: int
- Number of predictive variable(s) used in the subset model
Return:
- mallow_score: int
- Mallow's C_p score of the subset model
>> from RegscorePy import *
>> y = [1,2,3,4]
>> y_pred = [5,6,7,8]
>> p = 3
>> aic.aic(y, y_pred, p)
17.090354888959126
>>
>>
>> bic.bic(y, y_pred, p)
15.249237972318795
>>
>>
>> y_sub = [1,2,3,5]
>> k = 3
>> p = 2
>> mallow.mallow(y, y_pred, y_sub, k, p)
>> 0.015625
- This usage apply to python3. If you use python2, please run
from __future__ import divisionbefore run the function.
From root directory, run all test files in terminal:
python -m pytest
You also have the option to run individual test files by referencing its path. For example, if you want to test aic function, you can use the command below:
python -m pytest RegscorePy/test/test_aic.py
This is an open source project. Please follow the guidelines below for contribution.
- Open an issue for any feedback and suggestions.
- For contributing to the project, please refer to Contributing for details.