Machine Learning Methods that uses Gaussian Distribution
- Likelihood Modeling: In algorithms, such as linear regression, logistic regression, and Gaussian mixture models, it is often assumed that the observed data is generated from a Gaussian distribution. It simplifies the model and allows for efficient parameter estimation.
- Bayesian Inference: In Bayesian machine learning, the Gaussian distribution is commonly used as a prior distribution over model parameters. This prior distribution reflects about the parameters before observing any data and is updated to a posterior distribution using Bayes’ theorem.
- Clustering: Gaussian mixture models (GMMs) can model complex data distributions and are often used in image segmentation and data compression.
- Anomaly Detection: Gaussian distribution is often used in anomaly detection algorithms, where the goal is to identify rare events or outliers in the data. Anomalies are detected based on the likelihood of the data under the Gaussian distribution.
- Dimensionality Reduction: Principal Component Analysis (PCA), it finds the directions of maximum variance in the data, which correspond to the principal components.
- Kernel Methods: Gaussian kernel is commonly used in kernelized machine learning algorithms, such as Support Vector Machines (SVMs) and Gaussian Processes (GPs), to define the similarity between data points.
Gaussian Distribution In Machine Learning
The Gaussian distribution, also known as the normal distribution, plays a fundamental role in machine learning. It is a key concept used to model the distribution of real-valued random variables and is essential for understanding various statistical methods and algorithms.
Table of Content
- Gaussian Distribution
- Gaussian Distribution Curve
- Gaussian Distribution Table
- Properties of Gaussian Distribution
- Machine Learning Methods that uses Gaussian Distribution
- Implementation of Gaussian Distribution in Machine Learning
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