### What is the difference between Decision Trees, Bagging and Random Forest?

A decision tree serves as the building block of most bagging and boosting algorithms and is always built using the concept of maximizing information.

- Machine Learning 101 (30)
- Statistics 101 (38)
- Supervised Learning (114)
- Regression (42)
- Classification (46)
- Logistic Regression (10)
- Support Vector Machine (10)
- Naive Bayes (4)
- Discriminant Analysis (5)
- Classification Evaluations (9)

- Classification & Regression Trees (CART) (23)

- Unsupervised Learning (55)
- Clustering (28)
- Distance Measures (9)
- Dimensionality Reduction (9)

- Deep Learning (23)
- Data Preparation (34)
- General (5)
- Standardization (6)
- Missing data (7)
- Textual Data (16)

Bagging, or “Bootstrap Aggregation”, refers to an ensemble design structure in which each instance of the ensemble, such as an individual decision tree in a Random Forest, is created on a different subset of the original dataset.

Find out all the ways

that you can