From 3f8d4003cd5e051a77b111fbb47f2bed5da57fa8 Mon Sep 17 00:00:00 2001
From: SL345 <sherilyn345@gmail.com>
Date: Mon, 2 Jul 2018 16:37:11 +0000
Subject: [PATCH] Done

---
 __pycache__/__init__.cpython-36.pyc           | Bin 0 -> 147 bytes
 .../__pycache__/__init__.cpython-36.pyc       | Bin 0 -> 159 bytes
 q01_bagging/__pycache__/build.cpython-36.pyc  | Bin 0 -> 1382 bytes
 q01_bagging/build.py                          |  27 ++++++++++++++++++
 .../tests/__pycache__/__init__.cpython-36.pyc | Bin 0 -> 165 bytes
 .../test_q01_bagging.cpython-36.pyc           | Bin 0 -> 956 bytes
 6 files changed, 27 insertions(+)
 create mode 100644 __pycache__/__init__.cpython-36.pyc
 create mode 100644 q01_bagging/__pycache__/__init__.cpython-36.pyc
 create mode 100644 q01_bagging/__pycache__/build.cpython-36.pyc
 create mode 100644 q01_bagging/tests/__pycache__/__init__.cpython-36.pyc
 create mode 100644 q01_bagging/tests/__pycache__/test_q01_bagging.cpython-36.pyc

diff --git a/__pycache__/__init__.cpython-36.pyc b/__pycache__/__init__.cpython-36.pyc
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diff --git a/q01_bagging/__pycache__/__init__.cpython-36.pyc b/q01_bagging/__pycache__/__init__.cpython-36.pyc
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diff --git a/q01_bagging/build.py b/q01_bagging/build.py
index c34fb02..e7e2e50 100644
--- a/q01_bagging/build.py
+++ b/q01_bagging/build.py
@@ -1,3 +1,4 @@
+# %load q01_bagging/build.py
 import pandas as pd
 from sklearn.model_selection import train_test_split
 from sklearn.tree import DecisionTreeClassifier
@@ -12,7 +13,33 @@
 y = dataframe.iloc[:, -1]
 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=9)
 
+accuracy_train_list =[]
+accuracy_test_list =[]
+estimator_list = []
 
 # Write your code here
+def bagging(X_train,X_test, y_train, y_test,n_est):
+    for i in range(1,50):
+        bag_clf = BaggingClassifier(DecisionTreeClassifier(),
+                                  n_estimators=i,
+                                  max_samples = 0.67,
+                                  max_features = 0.67,
+                                  bootstrap = True,
+                                  random_state=9)
+        bag_clf.fit(X_train,y_train)
+        y_test_pred = bag_clf.predict(X_test)
+        bag_clf.fit(X_test,y_test)
+        y_train_pred = bag_clf.predict(X_train)
+        accuracy_test_list.append(accuracy_score(y_test,y_test_pred))
+        accuracy_train_list.append(accuracy_score(y_train,y_train_pred))
+        estimator_list.append(i)
 
+    accuracy_test_list.pop(0)    
+    accuracy_train_list.pop(0)
+    estimator_list.pop(0)    
+    plt.plot(estimator_list,accuracy_train_list)
+    plt.plot(estimator_list,accuracy_test_list)
+    plt.xlabel('n_estimator')
+    plt.ylabel('accuracy')
+    plt.show()
 
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