Collective Intelligence: Boost, Bag, and Stack for Powerful Machine Learning Model Combinations

btd
3 min readNov 16, 2023

Ensemble learning is a machine learning paradigm that involves combining the predictions of multiple models to improve overall performance, accuracy, and robustness. The idea is to leverage the strengths of individual models and compensate for their weaknesses, ultimately achieving better generalization and predictive power than any single model.

Here are key concepts and characteristics of ensemble learning:

I. Diversity of Models:

  • Ensemble methods often work best when the individual models (learners or base models) are diverse. Diversity is achieved by training models using different algorithms, subsets of data, or variations in model parameters.

II. Types of Ensemble Learning:

1. Bagging (Bootstrap Aggregating):

  • In bagging, multiple instances of a model are trained independently on different subsets of the training data, often created through bootstrapping (random sampling with replacement). The final prediction is typically an average or a vote among the individual model predictions. Random Forest is a well-known bagging algorithm.
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score

# Sample data
from sklearn.datasets import load_iris
iris = load_iris()
X, y = iris.data, iris.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 Random Forest classifier
rf_classifier = RandomForestClassifier(n_estimators=100, random_state=42)

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

# Make predictions
predictions = rf_classifier.predict(X_test)

# Evaluate accuracy
accuracy = accuracy_score(y_test, predictions)
print(f"Random Forest Accuracy: {accuracy}")

2. Boosting:

  • In boosting, models are trained sequentially, and each new model focuses on correcting the errors made by its predecessors. Boosting assigns weights to data points, giving more emphasis to misclassified instances in subsequent…

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