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Is your Jeffreys prior vague in what sense?
Jeffrey’s prior (also called Jeffreys-Rule Prior), named after English mathematician Sir Harold Jeffreys, is used in Bayesian parameter estimation. It is an uninformative prior, which means that it gives you vague information about probabilities.
What is uninformative prior?
An uninformative prior or diffuse prior expresses vague or general information about a variable. The term “uninformative prior” is somewhat of a misnomer. Such a prior might also be called a not very informative prior, or an objective prior, i.e. one that’s not subjectively elicited.
How does the size of the dataset affect the model?
Since the model tries to best fit the available training data, the quantity of data directly determines the split levels and final classes. From the above figure, we can clearly observe that the split points and final class predictions get greatly influenced by the size of the dataset.
What is the curse of small datasets in machine learning?
This is Part 1 of Breaking the curse of small datasets in Machine Learning. In this part, I will discuss how the size of the data set impacts traditional Machine Learning algorithms and few ways to mitigate these issues.
Is the unknown quantity an observable variable in the model?
The unknown quantity may be a parameter of the model or a latent variable rather than an observable variable . Bayes’ theorem calculates the renormalized pointwise product of the prior and the likelihood function, to produce the posterior probability distribution, which is the conditional distribution of the uncertain quantity given the data.
Why are unbalanced datasets bad for machine learning?
In the case of unbalanced data, the model becomes more biased towards the majority class as it has a larger influence on the final loss value and our model becomes less useful. In such scenarios, we can add weights to the losses corresponding to different classes to even out this data bias.
https://www.youtube.com/watch?v=TEe-t_rwuts