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Bayes' Theorem Explained #short #maths #probability #shortsfeed
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If you are beginning to learn machine learning, the number of model names can make the field seem much more complicated than it actually is, but most models can be organized into a few major families based on how they learn and the kinds of problems they are designed to solve. Linear and logistic regression learn relatively simple relationships between variables, while decision trees, random forests, and gradient-boosting models make predictions through combinations of decision rules. K-nearest
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Probability puzzle with bayes theorem – what’s the probability you picked a fair coin if you flip 10 heads in a row given a one and 10,000 chance that you picked a rigged coin that always flips heads. #Stats #Probability #Statistics
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Bayes Theorem is probably one of the most important things any rational person can learn, because so many of our debates and disagreements that we shout at each other about are because we don't understand how our beliefs work. Bayes Theorem goes back to the 18th century, Thomas Bayes, and essentially it's a formula which asks: when we are presented with the evidence for something, how much should we believe it? Bayes Theorem teaches us that our beliefs are not fixed. They are probabilities. Our
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TikTokphilosophyminis
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Why is Bayes' theorem true? One view is that it's what pops out when you unpack P(A and B) in two different ways.
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