**Random Forest** is a bagging ensemble algorithm that is used for both classification and regression tasks.  The basic idea behind Random Forest is to create a "forest" of decision trees, where each tree is trained on a different subset of the data and provides its own prediction. 

## How does Random Forest works?

1. **Bootstrapping and Data Subset**: Each tree in the forest is trained using a random subset drawn from the original dataset via bootstrapping. This process involves selecting data points **with replacement**, creating diverse subsets for each tree;

2. **Decision Tree Construction**: These subsets build individual decision trees. Data is recursively divided using features and thresholds, forming binary splits that lead to leaf nodes containing predictions;

3. **Random Feature Selection**: Within each tree, only a random subset of features is considered for creating splits. This randomness prevents single features from overpowering predictions and enhances tree diversity;

4. **Prediction Aggregation**: After training, each tree predicts for data points. For classification, we use hard or soft voting to create a prediction; for regression, predictions are averaged to provide the final outcome.

We can notice a rather interesting **feature of a random tree**: each base model is trained not only on a random subset of the training set, but also on a random subset of features. As a result, we get more independent base models and, as a result, more accurate final predictions.
## Example
Let's solve the classification task using Random Forest on Iris dataset: 

# Import necessary libraries
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import f1_score

# Load the Iris dataset
iris = load_iris()
X = iris.data  # Features
y = iris.target  # Target variable

# Split the dataset 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 Random Forest classifier
rf_classifier = RandomForestClassifier(n_estimators=100, n_jobs=-1)

# Train the classifier on the training data
rf_classifier.fit(X_train, y_train)

# Make predictions on the test data
y_pred = rf_classifier.predict(X_test)

# Calculate the F1 score of the classifier
f1 = f1_score(y_test, y_pred, average='weighted')
print(f'F1 Score: {f1:.2f}')

What model is used as a base model in Random Forest?

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.