From b75291370e58092d8949fe040587e488e3e092e4 Mon Sep 17 00:00:00 2001
From: tracedence <shoaibrashid887@gmail.com>
Date: Mon, 28 Jan 2019 15:31:57 +0000
Subject: [PATCH] Done

---
 __pycache__/__init__.cpython-36.pyc           | Bin 0 -> 152 bytes
 .../__pycache__/__init__.cpython-36.pyc       | Bin 0 -> 164 bytes
 q01_bagging/__pycache__/build.cpython-36.pyc  | Bin 0 -> 1389 bytes
 q01_bagging/build.py                          |  28 ++++++++++++++++++
 .../tests/__pycache__/__init__.cpython-36.pyc | Bin 0 -> 170 bytes
 .../test_q01_bagging.cpython-36.pyc           | Bin 0 -> 961 bytes
 6 files changed, 28 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 19f8726..5fe77e5 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
@@ -15,5 +16,32 @@
 
 
 # Write your code here
+def bagging(X_train, X_test, y_train, y_test, n_est ):
+    
+    test_score = []
+    train_score = []
+    n_esti = range(1,n_est)
+    for  i in n_esti:
+        bagging_clf2 = BaggingClassifier(DecisionTreeClassifier(), n_estimators=i, max_samples=0.67, 
+                                        bootstrap=True, random_state=9 ,max_features = 0.67,)
+
+        bagging_clf2.fit(X_train, y_train)
+        y_pred_bagging = bagging_clf2.predict(X_test)
+        test_score.append( accuracy_score(y_test, y_pred_bagging))
+
+        y_pred_bagging1 = bagging_clf2.predict(X_train)
+        train_score.append( accuracy_score(y_train, y_pred_bagging1))
+        
+    plt.plot(n_esti, test_score , label = 'Test_set')
+    plt.plot(n_esti,train_score, label = 'Train_set')
+    plt.xlabel('n_estimators')
+
+    plt.ylabel('accuracy')
+    plt.legend(loc=1)
+    plt.show()
+
+
+
+bagging(X_train, X_test, y_train, y_test, 50 )
 
 
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