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True Distribution in ML - Python Automation and Machine Learning for ICs - - An Online Book - |
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| Python Automation and Machine Learning for ICs http://www.globalsino.com/ICs/ | ||||||||
| Chapter/Index: Introduction | A | B | C | D | E | F | G | H | I | J | K | L | M | N | O | P | Q | R | S | T | U | V | W | X | Y | Z | Appendix | ||||||||
================================================================================= The true distribution is an idealized concept. It refers to the actual underlying distribution that generated the data. In many machine learning applications, we make assumptions about the true distribution, and our models are designed to learn this underlying distribution as closely as possible. However, in most cases, we can only work with observed data (a sample), and our models try to make inferences about the true distribution based on this limited data. ============================================
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