https://github.com/jkapila/vcvn
R package for Variable Selection, Curve Fitting, Variable Conversion and Normalisation
https://github.com/jkapila/vcvn
curve-fitting measure normalisation scoring variable-selection voting
Last synced: 9 days ago
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R package for Variable Selection, Curve Fitting, Variable Conversion and Normalisation
- Host: GitHub
- URL: https://github.com/jkapila/vcvn
- Owner: jkapila
- License: bsd-3-clause
- Created: 2017-08-16T12:31:08.000Z (almost 8 years ago)
- Default Branch: master
- Last Pushed: 2017-08-28T14:02:38.000Z (almost 8 years ago)
- Last Synced: 2025-06-03T00:04:36.930Z (26 days ago)
- Topics: curve-fitting, measure, normalisation, scoring, variable-selection, voting
- Language: R
- Homepage:
- Size: 23.4 KB
- Stars: 3
- Watchers: 3
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# vcvn
R package for Variable Selection, Curve Fitting, Variable Conversion, Normalisation and Accuracy Measures
This will include following:
## Variable Selection:
1) Information Value
2) Gini Index
3) Gini Impurity
4) Entropy Gain
5) Misclassification Error
## To be Implemented under Variable Selection Methods:
1) Variable Ranking Methods - voting / scoring / weighted scoring / weighted voting
2) Generic Scoring Function (for Regressiona and Classification)
3) Variable Inflation Factor
4) Other Variable Impacts for regression
## Curve Fitting(To be implemented):
1) Template for Curve Fitting for Contineous and Categorical Variable
2) Curve Comparision Methods
3) Curve Indentification
4) Curve Tuning
5) Curve to Normal Conversion
6) Non - Curve / Random / Many Matching curve Decision Criterion
7) Goodness of Fit Test:
a) Kolomogorov- Simronov Test
b) Carmer-Von Mises Test
c) Anderson-Darling Test
d) Shapiro -Wilk Test
e) Chi-Squared Test
f) Akaike Information Criterion (AIC)
g) Hosmer - Lemeshow Test
## Variable Conversion(To be implemented):
1) Continuous to Categorical
a) Range Binning
b) WOE Criterion Binning
c) Dependent Binning
2) Categrical to Contineous
a) One - Hot Encoding with and without reference
b) Label Encoding
c) Weightage Encoding
d) Boosted Encoding ( Based on CatBoost Methodology by Yandex)
## Normalisation (To be Implemented):
1) Unit Mean
2) Unit SD
3) Unit Mean And SD
4) Min - Max
5) Box-Cox
6) Log
7) Exponential
8) Mean Difference
9) Median Difference
10) Mean Difference wiht SD
11) Median Difference with SD
**Will also try to include predict function for applying variable conversion and normalisation on raw data.
## Measures (These are extensions for other calculations):
1) RMSE
2) MAE
3) MAPE
4) R-squared
5) AIC
6) BIC
7) AUC
## To be implemented Measures:
1) Kendall's Tau
2) Gini Index
3) Weights
4) Extension to caret's ConfusionMatrix
**Will also try to include methods for finding best and/or biased limit for probablity cut-off of calssification problem
## Updated as on 28th August, 2017