PR by sparshangre

README.md

@@ -4,14 +4,8 @@ Read [Generalized Linear Models (GLM)](http://www.wright.edu/~thaddeus.tarpey/ES
  * Check out this [deck](http://www.mc.vanderbilt.edu/gcrc/workshop_files/2004-11-12.pdf) introducing logistic regression.
  * Read [William King's logistic regression tutorial](http://ww2.coastal.edu/kingw/statistics/R-tutorials/logistic.html) with examples in `R`. It explains terms nicely and gives good illustrative examples.
 
-### Session Slides
-
-@[gslides](1VYmw480CTnVDPPG6E9rAFc9P1ukuMlg2ATCjEnO_BLY)
-
 ### Post Reads
 
-Optional:
-
  * Read [Generative and Discriminative Classifiers: Naive Bayes and Logistic Regression](http://www.cs.cmu.edu/~tom/mlbook/NBayesLogReg.pdf). This will likely help you to better understand both Naive Bayes and logistic regression, and how they can be thought of as related.
  * The UCLA Institute for Digital Research and Education has a lot of resources on using statistical software, such as: [R Data Analysis Examples: Logit Regression](http://www.ats.ucla.edu/stat/r/dae/logit.htm).
  * For a few general multiclass reduction approaches, read these papers on [Weighted One-Against All](http://hunch.net/~jl/projects/reductions/woa/woa.pdf) and [Error-Correcting Tournaments](http://hunch.net/~beygel/tournament.pdf).

build_model.py

@@ -13,7 +13,7 @@
 X_train, y_train = pickle.load(open(ROOT_DIR + "/data/german_train.p", "rb"))
 
 def build():
-    creditClf = RandomForestClassifier(n_estimators=20, random_state=1)
+    creditClf = LogisticRegression(random_state=1)
     creditClf.fit(X=X_train, y=y_train)
     return creditClf
 
@@ -26,4 +26,4 @@ def verify(clf, X, y):
 
 if __name__ == "__main__":
     creditClf = build()
-    verify(creditClf, X_train, y_train)
+    verify(creditClf, X_train, y_train)