AdaBoost Implementation Code

Below, I provide a simple AdaBoost code implementation that allows beginners to easily understand and run it directly. AdaBoost is an ensemble learning algorithm designed to enhance classifier accuracy. It operates by iteratively adjusting training dataset weights to train multiple weak classifiers and combines them into a strong classifier. The AdaBoost algorithm has been widely applied in machine learning fields including computer vision and natural language processing. Therefore, learning to implement the AdaBoost algorithm is highly valuable. Here is a simple AdaBoost code example for your reference.

```python

def adaboost(train_data, T):

# Initialize equal weights for all training samples

weight = np.ones(len(train_data)) / len(train_data)

# Train T weak classifiers sequentially

for t in range(T):

# Train a weak classifier using current sample weights

classifier = train_weak_classifier(train_data, weight)

# Calculate weighted error rate of the current classifier

error_rate = calculate_error_rate(classifier, train_data, weight)

# Compute classifier weight using logarithmic ratio of accuracy and error

classifier_weight = np.log((1 - error_rate) / error_rate) / 2

# Update sample weights: increase weights for misclassified samples

weight *= np.exp(-classifier_weight * train_data[:, -1] * classifier(train_data[:, :-1]))

# Normalize weights to maintain probability distribution

weight /= np.sum(weight)

# Return final strong classifier as weighted combination of weak classifiers

def classifier(x):

return np.sign(np.sum([np.log((1 - error_rate) / error_rate) / 2 * classifier(train_data[:, :-1]) for classifier, error_rate in classifiers]))

return classifier

```

This code demonstrates key AdaBoost components: weight initialization, iterative weak classifier training, error-based weight updates, and final classifier combination. The implementation uses numpy for efficient numerical operations and follows the standard AdaBoost mathematical formulation. We hope this code helps you better understand AdaBoost algorithm principles and implementation details.