Gaussian Distribution Curve
The curve is symmetric and bell-shaped, and it mathematically represents the probability distribution of a continuous random variable. The Gaussian distribution is characterized by two parameters: the mean (μ) and the standard deviation (σ), which determine the location and the spread of the curve.
- The standard deviations are used to subdivide the area under the normal curve. Each subdivided section defines the percentage of data, which falls into the specific region of a graph.
- Analysis : A smaller standard deviation results in a narrower and taller bell curve, indicating that data points are clustered closely around the mean. Conversely, a larger standard deviation leads to a wider and shorter bell curve, suggesting that data points are more spread out from the mean.
- The Empirical Rule, also known as the 68-95-99.7 rule, quantifies the proportion of data falling within certain intervals around the mean in a normal distribution. It provides a quick way to estimate the spread of data without performing detailed calculations.
- Within one standard deviation of the mean (Mean ± 1 SD), approximately 68% of the data is expected to fall.
- Within two standard deviations of the mean (Mean ± 2 SD), approximately 95% of the data is expected to fall.
- Within three standard deviations of the mean (Mean ± 3 SD), approximately 99.7% of the data is expected to fall.
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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