From 75a251ad147b828aedec8f13f561755668234ce7 Mon Sep 17 00:00:00 2001
From: sparshangre <sparsh.angre@gmail.com>
Date: Thu, 15 Jun 2017 12:05:00 +0530
Subject: [PATCH 1/3] Done

---
 build_model.py             |   4 ++--
 build_model.pyc            | Bin 0 -> 1558 bytes
 tests/__init__.pyc         | Bin 0 -> 165 bytes
 tests/test_build_model.pyc | Bin 0 -> 1590 bytes
 4 files changed, 2 insertions(+), 2 deletions(-)
 create mode 100644 build_model.pyc
 create mode 100644 tests/__init__.pyc
 create mode 100644 tests/test_build_model.pyc

diff --git a/build_model.py b/build_model.py
index 0990d56..350a827 100644
--- a/build_model.py
+++ b/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)
\ No newline at end of file
+    verify(creditClf, X_train, y_train)
diff --git a/build_model.pyc b/build_model.pyc
new file mode 100644
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From c1d5ae432ca545109785802cd9b28a4eb7defa65 Mon Sep 17 00:00:00 2001
From: sparshangre <sparsh.angre@gmail.com>
Date: Thu, 15 Jun 2017 12:06:04 +0530
Subject: [PATCH 2/3] Done

---
 README.md | 2 --
 1 file changed, 2 deletions(-)

diff --git a/README.md b/README.md
index 220f9fb..7252f04 100644
--- a/README.md
+++ b/README.md
@@ -10,8 +10,6 @@ Read [Generalized Linear Models (GLM)](http://www.wright.edu/~thaddeus.tarpey/ES
 
 ### 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).

From d17058fa3df608ec6d6d78b56ee96854b810e2ba Mon Sep 17 00:00:00 2001
From: sparshangre <sparsh.angre@gmail.com>
Date: Thu, 15 Jun 2017 12:09:52 +0530
Subject: [PATCH 3/3] Done

---
 README.md | 4 ----
 1 file changed, 4 deletions(-)

diff --git a/README.md b/README.md
index 7252f04..4760f85 100644
--- a/README.md
+++ b/README.md
@@ -4,10 +4,6 @@ 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
 
  * 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.