提升
定義和目的
Boosting 是機器學習中用於提高模型準確性的整合學習技術。它結合了多個弱分類器（性能比隨機猜測稍好的模型）來創建強分類器。 boosting的主要目的是依序將弱分類器應用到資料上，修正先前的分類器所犯的錯誤，從而提高整體效能。
主要目標：
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提高準確率：透過組合多個弱分類器的輸出來提高預測準確率。
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減少偏差和變異數：解決偏差和變異數問題，以實現模型更好的泛化。
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處理複雜資料：有效建模資料中的複雜關係。
AdaBoost（自適應增強）
定義和目的
AdaBoost，Adaptive Boosting 的縮寫，是一種流行的 boosting 演算法。它調整錯誤分類實例的權重，以便後續分類器更關注困難的情況。 AdaBoost 的主要目的是透過在每次迭代中強調難以分類的範例來提高弱分類器的效能。
主要目標：
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權重調整：增加錯誤分類實例的權重，以確保下一個分類器專注於它們。
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順序學習：順序建立分類器，每個新分類器都會修正其前一個分類器的錯誤。
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效能提升：將弱分類器組合起來形成具有更好預測能力的強分類器。
AdaBoost 的工作原理
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初始化權重:
- 為所有訓練實例分配相同的權重。對於包含 n 個實例的資料集，每個實例的權重為 1/n。
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訓練弱分類器:
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計算分類器錯誤:
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計算分類器權重:
- 根據分類器的誤差計算其權重。重量由下式給出：
阿爾法 = 0.5 * log((1 - 錯誤) / 錯誤)
- 較低的錯誤導致較高的分類器權重。
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更新實例的權重:
- 調整實例的權重。增加錯誤分類實例的權重，減少正確分類實例的權重。
- 實例 i 的更新權重為：
權重[i] = 權重[i] * exp(alpha * (錯誤分類? 1 : -1))
- 標準化權重以確保它們的總和為 1。
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組合弱分類器:
- 最終的強分類器是弱分類器的加權和：
最終分類器=符號(sum(alpha *weak_classifier))
- sign 函數根據總和決定類別標籤。
AdaBoost（二元分類）範例
AdaBoost 是 Adaptive Boosting 的縮寫，是一種結合多個弱分類器來創建強分類器的整合技術。此範例示範如何使用合成資料實現 AdaBoost 進行二元分類、評估模型的效能以及視覺化決策邊界。
Python 程式碼範例
1。導入庫
import numpy as np
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.ensemble import AdaBoostClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import accuracy_score, confusion_matrix, classification_report
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此區塊導入資料操作、繪圖和機器學習所需的庫。
2。產生樣本資料
np.random.seed(42) # For reproducibility
# Generate synthetic data for 2 classes
n_samples = 1000
n_samples_per_class = n_samples // 2
# Class 0: Centered around (-1, -1)
X0 = np.random.randn(n_samples_per_class, 2) * 0.7 + [-1, -1]
# Class 1: Centered around (1, 1)
X1 = np.random.randn(n_samples_per_class, 2) * 0.7 + [1, 1]
# Combine the data
X = np.vstack([X0, X1])
y = np.hstack([np.zeros(n_samples_per_class), np.ones(n_samples_per_class)])
# Shuffle the dataset
shuffle_idx = np.random.permutation(n_samples)
X, y = X[shuffle_idx], y[shuffle_idx]
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此區塊產生具有兩個特徵的合成數據，其中目標變數 y 是基於類別中心定義的，模擬二元分類場景。
3。分割資料集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
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此區塊將資料集拆分為訓練集和測試集以進行模型評估。
4。建立並訓練 AdaBoost 分類器
base_estimator = DecisionTreeClassifier(max_depth=1) # Decision stump
model = AdaBoostClassifier(estimator=base_estimator, n_estimators=3, random_state=42)
model.fit(X_train, y_train)
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此區塊使用決策樹樁作為基本估計器來初始化 AdaBoost 模型，並使用訓練資料集對其進行訓練。
5。做出預測
y_pred = model.predict(X_test)
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此區塊使用經過訓練的模型對測試集進行預測。
6。評估模型
accuracy = accuracy_score(y_test, y_pred)
conf_matrix = confusion_matrix(y_test, y_pred)
class_report = classification_report(y_test, y_pred)
print(f"Accuracy: {accuracy:.4f}")
print("\nConfusion Matrix:")
print(conf_matrix)
print("\nClassification Report:")
print(class_report)
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輸出：
Accuracy: 0.9400
Confusion Matrix:
[[96 8]
[ 4 92]]
Classification Report:
precision recall f1-score support
0.0 0.96 0.92 0.94 104
1.0 0.92 0.96 0.94 96
accuracy 0.94 200
macro avg 0.94 0.94 0.94 200
weighted avg 0.94 0.94 0.94 200
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此區塊計算並列印準確性、混淆矩陣和分類報告，提供對模型效能的深入了解。
7。可視化決策邊界
x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1
y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1
xx, yy = np.meshgrid(np.arange(x_min, x_max, 0.1),
np.arange(y_min, y_max, 0.1))
Z = model.predict(np.c_[xx.ravel(), yy.ravel()])
Z = Z.reshape(xx.shape)
plt.figure(figsize=(10, 8))
plt.contourf(xx, yy, Z, alpha=0.4, cmap='RdYlBu')
scatter = plt.scatter(X[:, 0], X[:, 1], c=y, cmap='RdYlBu', edgecolor='black')
plt.xlabel("Feature 1")
plt.ylabel("Feature 2")
plt.title("AdaBoost Binary Classification")
plt.colorbar(scatter)
plt.show()
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This block visualizes the decision boundary created by the AdaBoost model, illustrating how the model separates the two classes in the feature space.
Output:
This structured approach demonstrates how to implement and evaluate AdaBoost for binary classification tasks, providing a clear understanding of its capabilities. The visualization of the decision boundary aids in interpreting the model's predictions.
AdaBoost (Multiclass Classification) Example
AdaBoost is an ensemble learning technique that combines multiple weak classifiers to create a strong classifier. This example demonstrates how to implement AdaBoost for multiclass classification using synthetic data, evaluate the model's performance, and visualize the decision boundary for five classes.
Python Code Example
1. Import Libraries
import numpy as np
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.ensemble import AdaBoostClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import accuracy_score, confusion_matrix, classification_report
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This block imports the necessary libraries for data manipulation, plotting, and machine learning.
2. Generate Sample Data with 5 Classes
np.random.seed(42) # For reproducibility
n_samples = 2500 # Total number of samples
n_samples_per_class = n_samples // 5 # Ensure this is exactly n_samples // 5
# Class 0: Centered around (-2, -2)
X0 = np.random.randn(n_samples_per_class, 2) * 0.5 + [-2, -2]
# Class 1: Centered around (0, -2)
X1 = np.random.randn(n_samples_per_class, 2) * 0.5 + [0, -2]
# Class 2: Centered around (2, -2)
X2 = np.random.randn(n_samples_per_class, 2) * 0.5 + [2, -2]
# Class 3: Centered around (-1, 2)
X3 = np.random.randn(n_samples_per_class, 2) * 0.5 + [-1, 2]
# Class 4: Centered around (1, 2)
X4 = np.random.randn(n_samples_per_class, 2) * 0.5 + [1, 2]
# Combine the data
X = np.vstack([X0, X1, X2, X3, X4])
y = np.hstack([np.zeros(n_samples_per_class),
np.ones(n_samples_per_class),
np.full(n_samples_per_class, 2),
np.full(n_samples_per_class, 3),
np.full(n_samples_per_class, 4)])
# Shuffle the dataset
shuffle_idx = np.random.permutation(n_samples)
X, y = X[shuffle_idx], y[shuffle_idx]
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This block generates synthetic data for five classes located in different regions of the feature space.
3. Split the Dataset
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
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This block splits the dataset into training and testing sets for model evaluation.
4. Create and Train the AdaBoost Classifier
base_estimator = DecisionTreeClassifier(max_depth=1) # Decision stump
model = AdaBoostClassifier(estimator=base_estimator, n_estimators=10, random_state=42)
model.fit(X_train, y_train)
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This block initializes the AdaBoost classifier with a weak learner (decision stump) and trains it using the training dataset.
5. Make Predictions
y_pred = model.predict(X_test)
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This block uses the trained model to make predictions on the test set.
6. Evaluate the Model
accuracy = accuracy_score(y_test, y_pred)
conf_matrix = confusion_matrix(y_test, y_pred)
class_report = classification_report(y_test, y_pred)
print(f"Accuracy: {accuracy:.4f}")
print("\nConfusion Matrix:")
print(conf_matrix)
print("\nClassification Report:")
print(class_report)
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Output:
Accuracy: 0.9540
Confusion Matrix:
[[ 97 2 0 0 0]
[ 0 92 3 0 0]
[ 0 4 92 0 0]
[ 0 0 0 86 14]
[ 0 0 0 0 110]]
Classification Report:
precision recall f1-score support
0.0 1.00 0.98 0.99 99
1.0 0.94 0.97 0.95 95
2.0 0.97 0.96 0.96 96
3.0 1.00 0.86 0.92 100
4.0 0.89 1.00 0.94 110
accuracy 0.95 500
macro avg 0.96 0.95 0.95 500
weighted avg 0.96 0.95 0.95 500
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This block calculates and prints the accuracy, confusion matrix, and classification report, providing insights into the model's performance.
7. Visualize the Decision Boundary
x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1
y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1
xx, yy = np.meshgrid(np.arange(x_min, x_max, 0.1),
np.arange(y_min, y_max, 0.1))
Z = model.predict(np.c_[xx.ravel(), yy.ravel()])
Z = Z.reshape(xx.shape)
plt.figure(figsize=(12, 10))
plt.contourf(xx, yy, Z, alpha=0.4, cmap='viridis')
scatter = plt.scatter(X[:, 0], X[:, 1], c=y, cmap='viridis', edgecolor='black')
plt.xlabel("Feature 1")
plt.ylabel("Feature 2")
plt.title("AdaBoost Multiclass Classification (5 Classes)")
plt.colorbar(scatter)
plt.show()
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This block visualizes the decision boundaries created by the AdaBoost classifier, illustrating how the model separates the five classes in the feature space.
Output:
This structured approach demonstrates how to implement and evaluate AdaBoost for multiclass classification tasks, providing a clear understanding of its capabilities and the effectiveness of visualizing decision boundaries.
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