Gaussian naive Bayes - explained with a simple example

Gaussian naive Bayes
1. Introduction
2. Example with one variable (01:05)
3. Prior knowledge (05:51)
4. Example with two variables (07:00)

[@MonikaaVela]

Basic example but good ! thanks :)

[@Daniel-nm4kt]

This channel is the best. This is where I really learn Statistics. Keep up the good work.

[@meshackamimo1945]

Profam so happy I keep learning simpler methods to solve problems from u...kindly do a talk on bayesian filters and kalman filters..the very basics of the concepts

[@relegunet]

Excelente explicación, gracias por compartir tu conocimiento, me ayudo a entender mejor la relación que existe entre dos variables continuas. 👍

[@jagajaga6908]

what a wonderful explanation, thank you so much!!!

[@drfknoble]

A very clear and well illustrated example and explanation. Great work!

[@好了-t4d]

i think this channel should get more attention it provides the core infos in statistic with subtitle in the vid thanks asual

[@JakubHojsan]

I have never met a more goated individual.

[@ramakanthrama8578]

These videos are very helpful!

[@ShalinSingh-h2j]

2:12 Why is your standard deviation for x1 = 0.61? If you sum [2.5, 2.0, 1.7, 1.4, 1.2, 0.9, 0.8] you get 10.5, then summing the squared differences gets you 0.32, then the square root of that is 0.56. I didn't get 0.61 for my standard deviation calculation of those samples.

[@tilestats]

If you compute the SD for a sample, you calculate the sample standard deviation by dividing by n-1 (not n): kzbin.info/www/bejne/pn2rYoR3aatsq6csi=fxdiTBQDmUv3SlRo

[@ShalinSingh-h2j]

@@tilestats Oh! Good to know, thanks!

[@hubabokuti]

I get different standard deviation values, the means are in line. Great video by the way...

[@tilestats]

Can you show your calculations?

[@hubabokuti]

Here you go, I may be wrong of course np.sqrt(clf_gsn.fit(X_cancer, y_cancer).sigma_)) @@tilestats

[@tilestats]

Simply calculate the SD of each group like: import numpy as np X_cancer = np.array([4.1, 3.4, 2.9, 2.8, 2.7, 2.1, 1.6]) y_cancer = np.array([2.5, 2.0, 1.7, 1.4, 1.2, 0.9, 0.8]) print(np.std(X_cancer)) print(np.std(y_cancer))

[@hubabokuti]

I get 0.756 and 0.566 the same as before, but that is not what is in the video, they are 0.82 and 0.61 there@@tilestats