update2

README.md

@@ -22,6 +22,7 @@
    - 이 메타 모델이 최종 예측을 생성한다.
 
 
+---
 
 ### 배깅(Bagging) 예제 코드[¶]()
 
@@ -64,7 +65,7 @@
     plt.show()
 ````
 
-![result](./images/11_2.png)
+![result](./images/11_1.png)
 
 </div>
 
@@ -190,3 +191,127 @@
 
 </div>
 </details>
+
+---
+
+### 부스팅(Boosting) 예제 코드[¶]()
+
+<details>
+<summary>Code View</summary>
+
+<summary>AdaBoost - Regression</summary>
+<div markdown="1">
+  
+````python
+    import numpy as np
+    import matplotlib.pyplot as plt
+    from sklearn.tree import DecisionTreeRegressor
+    from sklearn.ensemble import AdaBoostRegressor
+
+    rng = np.random.RandomState(1)
+    X = np.linspace(0, 6, 100)[:, np.newaxis]
+    y = np.sin(X).ravel() + np.sin(6*X).ravel() + rng.normal(0, 0.1, X.shape[0])
+
+    regr_1 = DecisionTreeRegressor(max_depth=4)
+    regr_2 = AdaBoostRegressor(DecisionTreeRegressor(max_depth=4), n_estimators=100, random_state=rng)
+
+    regr_1.fit(X, y)
+    regr_2.fit(X, y)
+    y_1 = regr_1.predict(X)
+    y_2 = regr_2.predict(X)
+
+    plt.figure()
+    plt.scatter(X, y, c="k", label="training samples")
+    plt.plot(X, y_1, c="g", label="n_estimators=1", linewidth=2)
+    plt.plot(X, y_2, c="r", label="n_estimators=100", linewidth=2)
+    plt.xlabel("data")
+    plt.ylabel("target")
+    plt.title("AdaBoost Regression")
+    plt.legend()
+    plt.show()
+````
+
+![result](./images/11_2.png)
+
+</div>
+
+<summary>Gradient Boosting - Regression</summary>
+<div markdown="1">
+  
+````python
+import numpy as np
+import pandas as pd
+from sklearn import datasets
+import matplotlib.pyplot as plt
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import mean_squared_error
+from sklearn import ensemble
+from sklearn.metrics import mean_squared_error, r2_score
+from sklearn.model_selection import cross_val_predict
+
+boston = datasets.load_boston() # Boston 집값 데이터, 13개 속성, 마지막 중간값 정보
+print(boston.data.shape, boston.target.shape)
+print(boston.feature_names)
+
+data = pd.DataFrame(boston.data, columns=boston.feature_names)
+data = pd.concat([data, pd.Series(boston.target, name='MEDV')], axis=1)
+print(data.head())
+X = data.iloc[:,:-1]
+y = data.iloc[:,-1]
+x_training_set, x_test_set, y_training_set, y_test_set = train_test_split(X, y, test_size=0.10, random_state=42, shuffle=True)
+````
+
+</div>
+
+<summary>Result</summary>
+<div markdown="1">
+
+````planetext
+    (506, 13) (506,)
+    ['CRIM' 'ZN' 'INDUS' 'CHAS' 'NOX' 'RM' 'AGE' 'DIS' 'RAD' 'TAX' 'PTRATIO'
+    'B' 'LSTAT']
+        CRIM    ZN  INDUS  CHAS    NOX     RM   AGE     DIS  RAD    TAX   
+    0  0.00632  18.0   2.31   0.0  0.538  6.575  65.2  4.0900  1.0  296.0  
+    1  0.02731   0.0   7.07   0.0  0.469  6.421  78.9  4.9671  2.0  242.0   
+    2  0.02729   0.0   7.07   0.0  0.469  7.185  61.1  4.9671  2.0  242.0   
+    3  0.03237   0.0   2.18   0.0  0.458  6.998  45.8  6.0622  3.0  222.0   
+    4  0.06905   0.0   2.18   0.0  0.458  7.147  54.2  6.0622  3.0  222.0   
+
+    PTRATIO       B  LSTAT  MEDV  
+    0     15.3  396.90   4.98  24.0  
+    1     17.8  396.90   9.14  21.6  
+    2     17.8  392.83   4.03  34.7  
+    3     18.7  394.63   2.94  33.4  
+    4     18.7  396.90   5.33  36.2 
+````
+
+</div>
+
+<div markdown="1">
+  
+````python
+params = {'n_estimators':500, 'max_depth':4, 'min_samples_split':2, 'learning_rate':0.01, 'loss':'ls'}
+model = ensemble.GradientBoostingRegressor(**params)
+model.fit(x_training_set, y_training_set)
+model_score = model.score(x_training_set, y_training_set)
+print('R2 sq: ', model_score)
+
+y_predicted = model.predict(x_test_set)
+print('Mean squared error: %.2f'% mean_squared_error(y_test_set, y_predicted))
+print('Test Variance score: %.2f' % r2_score(y_test_set, y_predicted))
+
+fig, ax = plt.subplots()
+ax.scatter(y_test_set, y_predicted, edgecolors=(0,0,0))
+ax.plot([y_test_set.min(), y_test_set.max()], [y_test_set.min(), y_test_set.max()], 'k--', lw=4)
+ax.set_xlabel('Actual')
+ax.set_ylabel('Predicted')
+ax.set_title('Ground Truth vs Predicted')
+plt.show()
+````
+
+![result](./images/11_3.png)
+
+</div>
+
+
+</details>