Bayes Classifiers (1)

[@MassuNoEgao]

Thank you for this tutorial! The examples you used were very easy to understand. It really helped me get the concept.

[@tiagozortea]

Thanks Alexander! Very good videos and crystal clear explanations.

[@SamMoore19]

0:20 a bayesic classifier... get it

[@gabrielintriagov]

Should you use 1/(n-1) to calculate variances, instead of 1/(n)

[@vijayendrasdm]

Hi very nice lecture. I have a doubt. if x is discrete then we can only model it like the tabular method (i.e counting the instances). Are there any other ways of modeling discrete data?

[@AlexanderIhler]

Thanks. Of course, that's more like a question than a doubt. :) You can use any discrete distribution you like -- but besides categorical (tabular) distributions, these really only make sense when there is some underlying structure to the discrete variable. For example, if it represents a discrete count, the Poisson distribution is probably the most common choice.

[@vijayendrasdm]

Alexander Ihler thanks

[@richatiwari4043]

Good explanation. Thanks :)

[@socialriyad]

Please do a lecture on Logisitic Regression.

[@Zzzz-hk5ft]

Definitely turn the speed up to 1.5x

[@yomholedet]

realy good example