课程: Machine Learning Foundations: Probability
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The continuous probability distribution - Python教程
课程: Machine Learning Foundations: Probability
The continuous probability distribution
- [Presenter] We have discovered the probabilities of uncertain discreet quantities, however, many real-life events are continuous. For example, we could measure the time taken to finish the ticket at a software development project and obviously, there are infinite number of possible ways to complete a task so the measurement is continuous. Or the square footage of a randomly selected three-bedroom apartment. In ML, continuous probability distributions are of fundamental importance: starting in the models themselves, when estimating the mapping between the inputs and outputs, the distribution of input variables to the models, and the distribution of errors made by models. Let's dive into the key properties of continuous probability distributions. Unlike discrete probability distribution where we assign the probability to specific integer value, in continuous distribution, the probability of selecting a particular value…
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