tag:blogger.com,1999:blog-3302102753324425376.post1813502340932127961..comments2023-12-21T04:36:53.782-08:00Comments on 51 Elliot: Machine Learning in JavaScriptDarrenhttp://www.blogger.com/profile/00230771763285373052noreply@blogger.comBlogger2125tag:blogger.com,1999:blog-3302102753324425376.post-66792140170420550362013-11-05T08:02:48.521-08:002013-11-05T08:02:48.521-08:00Hi Cam
I should update that slide. Those three dot...Hi Cam<br />I should update that slide. Those three dots "..." are meant to indicate that there are some assumptions and calculations leading to a naive Bayesian classifier of the sort used for spam filtering. The title doesn't really make that clear.Darrenhttps://www.blogger.com/profile/00230771763285373052noreply@blogger.comtag:blogger.com,1999:blog-3302102753324425376.post-78668665837227086572013-10-30T02:41:41.152-07:002013-10-30T02:41:41.152-07:00Hi Darren,
The slide that shows Bayes Theorem as ...Hi Darren,<br /><br />The slide that shows Bayes Theorem as the following is in error:<br /><br />P(B | A)P(A) / (P(B | A)P(A)+(1−P(B | A)) (1−P(A)))<br /><br />as:<br /><br />P(B | not A) isn't equal to (1−P(B | A).<br /><br />The degree to which evidence is expected on one hypothesis P(B|A) isn't necessarily correlated with the degree to which the evidence is expected on another hypothesis P(B|not A).<br /><br />For an example, a manufacturer makes dice with either (1,2,3,4,5,6) or (1,2,2,4,5,6) on them. Let's name them type 1 and type 2 respectively.<br /><br />We choose a die from a bag with 50% chance of getting either type of dice.<br /><br />The question is what is P( roll a 2 | type 1 ) and what is P( roll a 2 | type 2). The answers are 1/6 and 1/3.<br /><br />Regards<br /><br />camspiershttps://www.blogger.com/profile/14936386580500788737noreply@blogger.com