**AdaBoost** is an ensemble learning algorithm that focuses on improving the performance of weak learners. It works by iteratively training a sequence of weak classifiers on **weighted versions of the training data**. The final prediction is a weighted combination of the predictions made by these weak classifiers. AdaBoost assigns higher weights to the misclassified samples, allowing subsequent models to concentrate on the difficult-to-classify instances.

## How AdaBoost Works? 

1. **Initialize Weights**: Assign equal weights to all training samples.
2. **Train Weak Classifier**: Train a weak classifier on the training data using the current sample weights. The weak classifier aims to minimize the weighted error rate, where the weights emphasize misclassified samples.
3. **Compute Classifier Weight**: Calculate the weight of the trained classifier based on its accuracy. Better classifiers are assigned higher weights.
4. **Update Sample Weights**: Update the sample weights, giving higher weights to the misclassified samples from the current classifier.
5. **Repeat**: Repeat steps 2-4 for a predefined number of iterations (or until a certain threshold is met).
6. **Final Prediction**: Combine the predictions of all weak classifiers by summing the weighted predictions. The class with the majority vote becomes the final prediction.
## Example
We can use `AdaBoostClassifier` class in Python to train AdaBoost model and provide predictions on real data:

from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import AdaBoostClassifier
from sklearn.metrics import f1_score

# Load the Iris dataset
data = load_iris()
X = data.data
y = data.target

# Split the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Create a Logistic Regression base model
base_model = LogisticRegression()

# Create and train the AdaBoostClassifier with Logistic Regression as base model
classifier = AdaBoostClassifier(base_model, n_estimators=50)
classifier.fit(X_train, y_train)

# Make predictions
y_pred = classifier.predict(X_test)

# Calculate F1 score
f1 = f1_score(y_test, y_pred, average='weighted')
print(f'F1 Score: {f1:.4f}')

Is the following statement true: AdaBoost assigns higher weights to the samples that were CORRECTLY classified?

Ensemble Learning is an advanced machine learning technique that combines multiple models to improve overall predictive performance and decision-making when solving real-life tasks. 

What is an ensemble? How are ensembles different from standard machine-learning models? What are the types of ensembles? Let's consider the answers to these questions.

Let's consider some commonly used bagging ensemble models, the features of their use, and also apply some of them to solve real-life tasks.

The mechanism of work of boosting models differs from bagging models. Now we will explore these distinctions, gain insights into utilizing model boosting for problem-solving, and illustrate its functionality through practical demonstrations.

Let's consider some commonly used stacking ensemble models, the features of their use, and also apply some of them to solve real-life tasks.

Ensemble Learning

AdaBoost Classifier

How AdaBoost Works?

Example

Is the following statement true: AdaBoost assigns higher weights to the samples that were CORRECTLY classified?