Man, i thought my english deepthroats
Hi , how can wij implement thesis algorithm that you mentioned te the movie to create patterns.
Awesome movie. Truly stijlvol and effortless to understand. One more subscriber for you.!
Fine explanation! Thank you!
why is correlated gegevens worthless ? why do wij have to make it uncorrelated ?
Excellent explanation, spil usual. Thank you!
Nicely explained! But I still have one question. Te transformation process you said ",using two correlated variables is futile", I am not sure I understand why? Thanks.
Very useful movie. thanks a lotsbestemming
Thanks for sharing This will help all fresh inwendig
Good movie! Elementary and efficient!
your accent is fine bro <,Three
No volume ter this movie.. can you help !!
It is well explained but super boring.Make it more frenetic pls
it seems very common sensical
Thales Sehn Korting tq a lotsbestemming for explanation but I have some informatie confused and still not got it yet I need like examples ! How the Gegevens mining work te our life or figures to waterput some examples names of animles and voorstelling us how to work also My warm regards
Thales, ficou showcase, se tem este material em portugues? Uma sugestaozinha. na parte den transformacao. eu explicaria o que voce colocou nos comentarios abaixo, pois do jeito que aparece no movie nao fica claro, o resto esta bem claro. Abracos!!
For elementary explanation of concept te gegevens mining visit my bloghttps://dataminingsimpleexplanations.blogspot.te/
Too abstract for mij to understand why thats gegevens processed ter very first place? Is that something useful to bitcoin network or just random gegevens?
im not from this area but found the tutorial very elementary and good , thank you.
Excellent movie got mij up speed quickly!
Thanks Thales for a superb movie. It wasgoed brief and to the point. I will be pointing my students here.One question I see that you get overheen and overheen again is about the problems of correlated gegevens. Another reason to find uncorrelated gegevens is that many skill discovery engines and instruments, such spil neural networks and almost every statistical package do not work with with very correlated variables. That is because if you end up using two or more correlated variables te a prediction algorithm, the correlated variables tend to bias the direction of the prediction to be based on what those variables represent. So if you are using five variable, three correlated and two independent, then the three correlated variables own 60% of the influence on the outcome (potentially, assuming the prediction engine starts with balanced effect).
NICE this REALY helped with my homework.i liked and subscribed
Dear Suppose you have Trio variables with the following feature values:x1 = [1, Two, Five, 6]x2 = [Four, Two, 7, 9]x3 = [1, Two, Five, 6]x1 and x3 are very correlated (i.e. equal te this case). Then it is not efficient to any algorithm to use both variables, since computations of distances and so on will terugwedstrijd equal values. So the proposal here is to use only x1 and x2.Regards
what a excellent movie !! thank you tormentor for making this awsome presentation , i became a fan truly 🙂
Indeed good movie explanation of gegevens mining. Plain to understand. Thank you Thales
Hi how can you determine from dataset that has 1000 rows and 21 columns which to use depending on your research are relevant. How can I reduce the gegevens. What are the effects attempting and need to create predictions regression and decision tree can you please help