Ensemble Learning

Ensemble Learning #

Ensemble Learning is a machine learning approach where we combine multiple models to produce a stronger final prediction.

Instead of depending on one model, an ensemble uses a group of models and combines their outputs.

The main idea is simple:

Many weak or moderately good models can work together to produce a better and more stable model.

Key takeaway:
Ensemble Learning improves prediction by combining several models.

Unsupervised Learning is used when we have input data but no target labels.

The model is not told the correct answer. Instead, it tries to discover hidden structure in the data.

Aspect	Supervised Learning	Unsupervised Learning
Data contains target label?	Yes	No
Learns from	Input-output pairs	Input features only
Main goal	Predict output	Discover structure
Example task	Classification, regression	Clustering
Example algorithm	Logistic regression, decision tree	K-means, GMM

Works on unlabelled raw data.
The algorithm discovers hidden patterns without prior knowledge of outcomes.
Requires no human intervention during training.
Does not make direct predictions — it groups or organises data instead.
Carries a higher risk because there’s no ground truth to verify results.
Common techniques include Clustering, Association, and Dimensionality Reduction.

The most common example is clustering, where similar records are grouped together.