Statistical learning is a branch of machine learning that focuses on extracting patterns from data using statistical methods. It is widely used in various fields such as finance, healthcare, and marketing.
Key Concepts
Supervised Learning: This is a type of statistical learning where the model is trained on labeled data. The goal is to learn a mapping from input variables to output variables.
Unsupervised Learning: This type of learning is used when the data does not have labels. The goal is to find patterns or structures in the data.
Reinforcement Learning: This is a type of learning where an agent learns to make decisions by taking actions in an environment and receiving rewards or penalties.
Applications
- Predictive Analytics: Used to predict future events based on historical data.
- Clustering: Grouping similar data points together.
- Classification: Assigning data points to predefined categories.
Resources
For more in-depth understanding of statistical learning, you can check out the following resources:
Image
Here's an image representing a machine learning model in action: