A common example of shallow learning is Linear Regression.
Shallow learning refers to traditional machine learning algorithms that operate on simpler network architectures or models, typically processing data through one or a few layers of computation. Unlike deep learning models with many hidden layers, shallow learning models have limited capacity to learn complex patterns directly from raw data without extensive feature engineering.
According to the provided reference, there are many shallow learning algorithms which have been applied over a vast time span. Examples include:
- Linear Regression: Used for predicting a continuous output variable based on one or more input features.
- Logistic Regression: Used for binary classification problems.
- Support Vector Machines (SVM): Effective for classification and regression tasks, especially in high-dimensional spaces.
- Decision Trees (DT): Tree-like models used for both classification and regression.
- K-nearest Neighbors (KNN): A simple algorithm that classifies data points based on the majority class of their 'k' nearest neighbors.
- Shallow Neural Networks: Neural networks with only one or a few hidden layers.
Here is a simple table summarizing some common shallow learning examples:
| Algorithm | Typical Use Case | Notes |
|---|---|---|
| Linear Regression | Predicting numerical values | Assumes linear relationship between features and target. |
| Logistic Regression | Binary Classification | Outputs probability. |
| Support Vector Machines | Classification, Regression | Effective with clear margin of separation. |
| Decision Trees | Classification, Regression | Easy to interpret. |
| K-nearest Neighbors | Classification, Regression | Instance-based, sensitive to feature scaling. |
These algorithms are considered "shallow" because they typically don't involve the deep, multi-layered processing that is characteristic of deep neural networks. They often require careful feature selection or engineering to perform well.
