From the course: Machine Learning Foundations: Probability

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The binomial distribution

The binomial distribution - Python Tutorial

From the course: Machine Learning Foundations: Probability

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The binomial distribution

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- [Instructor] Imagine we are working on an engine for a new electric car and we run 10 tests. The outcomes of these tests are two failures and eight successes. Management expected at least 90% success rate, and are disappointed, and expect the whole team to re-engineer the design and get back with a new model. But our team is confident we should run more tests and that their underlining probability is 95%. The type of discrete distribution we can apply here is called a binomial distribution. It represents the expected number of events happening in a given number of trials, if each event has a specified probability. We can apply it by using a simple formula, where P represents the probability of k events happening n trials. P of X equals k, equals n choose k, multiplied by P to the power of k, multiplied by one minus P to the power of n minus k. n choose k equals n factorial divided by k factorial, multiplied by n minus…

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