Merge pull request #5 from UBC-MDS/jill-dev

Add feature descriptions to readme

LICENSE

@@ -0,0 +1,29 @@
+BSD 3-Clause License
+
+Copyright (c) 2018, Master of Data Science at the University of British Columbia
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are met:
+
+* Redistributions of source code must retain the above copyright notice, this
+  list of conditions and the following disclaimer.
+
+* Redistributions in binary form must reproduce the above copyright notice,
+  this list of conditions and the following disclaimer in the documentation
+  and/or other materials provided with the distribution.
+
+* Neither the name of the copyright holder nor the names of its
+  contributors may be used to endorse or promote products derived from
+  this software without specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

README.md

@@ -1,42 +1,40 @@
 # PyPunisher
 
-The PyPunisher package will implement techniques for feature and model selection.
-Namely, it will contain tools for forward and backward selection, as well as tools for computing
-AIC and BIC (see below). 
+The PyPunisher package will implement techniques for feature and model selection. Namely, it will contain tools for forward and backward selection, as well as tools for computing AIC and BIC (see below). 
 
 
 ## Contributors: 
 
-Avinash
-Tariq
-Jill
-
-## Functions included:
+Avinash, Tariq, Jill
 
-- Forward selection 
-    * `forward_selection()`
-- Backward selection
-    * `backwards_selection()`
-- Metrics: 
-    - '`aic()`': Computes the Akaike information criterion [AIC](https://en.wikipedia.org/wiki/Akaike_information_criterion)
-    - '`bic()`: Computes the [Bayesian_information_criterion](https://en.wikipedia.org/wiki/Bayesian_information_criterion) 
 
 ## ToDos:
 
 Jill
 
-    * function description
+* function description
 
 Tariq
 
-    * summary paragraph
+* summary paragraph
 
 Avinash 
 
-    * where your packages fit into the Python and R ecosystems
+* where your packages fit into the Python and R ecosystems
+
 
+## Functions included:
+
+We will be implementing two stepwise feature selection techniques:
+
+- `forward_selection()`: a feature selection method in which you start with a null model and iteratively add useful features 
+- `backward_selection()`: a feature selection method in which you start with a full model and iteratively remove the least useful feature at each step
+
+We will also be implementing metrics that evaluate model performance: 
+
+- `aic()`: computes the Akaike information criterion [Akaike information criterion](https://en.wikipedia.org/wiki/Akaike_information_criterion)
+- `bic()`: computes the [Bayesian information criterion](https://en.wikipedia.org/wiki/Bayesian_information_criterion) 
 
-**Due**: Sunday Feb 11, 2018.
 
 
 ## How the packages fit into the existing R and Python ecosystems.
@@ -45,3 +43,4 @@ In Python ecosystem, forward selection has been implemented in scikit learn by t
 [f_regression](http://scikit-learn.org/stable/modules/generated/sklearn.feature_selection.f_regression.html) function. The function uses Linear model for testing the individual effect of each of many regressors. It has been implemented as a scoring function to be used in feature seletion procedure. The backward selection has also been implemented in scikit learn by the [RFE](http://scikit-learn.org/stable/modules/generated/sklearn.feature_selection.RFE.html) function. RFE uses an external estimator that assigns weights to features and it prunes the number of features by recursively considering smaller and smaller sets of features until the desired number of features to select is eventually reached. Whereas, in R ecosystem, forward and backward selection are implemented by [olsrr package](https://cran.r-project.org/web/packages/olsrr/)
 and in [MASS package](https://cran.r-project.org/web/packages/MASS/MASS.pdf) by function
 [StepAIC](https://stat.ethz.ch/R-manual/R-devel/library/MASS/html/stepAIC.html). StepAIC performs stepwise selection (forward, backward, both) by exact AIC.
+