From 821688b778e5d08a357da2637abe1237e5e097ba Mon Sep 17 00:00:00 2001
From: sanchitaharlalka <Sanchitaharlalka@gmail.com>
Date: Sat, 30 Jun 2018 08:37:39 +0000
Subject: [PATCH] Done

---
 __pycache__/__init__.cpython-36.pyc           | Bin 0 -> 158 bytes
 .../__pycache__/__init__.cpython-36.pyc       | Bin 0 -> 170 bytes
 q01_bagging/__pycache__/build.cpython-36.pyc  | Bin 0 -> 1285 bytes
 q01_bagging/build.py                          |  24 ++++++++++++++++++
 .../tests/__pycache__/__init__.cpython-36.pyc | Bin 0 -> 176 bytes
 .../test_q01_bagging.cpython-36.pyc           | Bin 0 -> 967 bytes
 6 files changed, 24 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

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diff --git a/q01_bagging/build.py b/q01_bagging/build.py
index c34fb02..0819cf0 100644
--- a/q01_bagging/build.py
+++ b/q01_bagging/build.py
@@ -1,3 +1,4 @@
+
 import pandas as pd
 from sklearn.model_selection import train_test_split
 from sklearn.tree import DecisionTreeClassifier
@@ -14,5 +15,28 @@
 
 
 # Write your code here
+def bagging(X_train, X_test, y_train, y_test,n_est):
+    accuracy_scores=[]
+    accuracy_scores_t=[]
+    n_est_list=[]
+    for n in list(range(1,n_est,1)):
+        bagging_clf1 = BaggingClassifier(DecisionTreeClassifier(), n_estimators=n, max_samples=0.67,max_features=0.67, 
+                                    bootstrap=True, random_state=9)
+        bagging_clf1.fit(X_train, y_train)
+        y_pred_bagging = bagging_clf1.predict(X_test)
+        score_bc_lr = accuracy_score(y_test, y_pred_bagging)
+        accuracy_scores.append(score_bc_lr)
+        
+        y_pred_bagging_t = bagging_clf1.predict(X_train)
+        score_bc_lr_t = accuracy_score(y_train, y_pred_bagging_t)
+        accuracy_scores_t.append(score_bc_lr_t)
+        n_est_list.append(n)
+        
+    plt.plot(n_est_list,accuracy_scores_t,accuracy_scores)
+    #plt.legend('Train Set','Test Set')
+    plt.show()
+
+#bagging(X_train, X_test, y_train, y_test,50)
+
 
 
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