As systematic traders, we are often working with tens of thousands of features where we need to find out what are the most predictive, uncorrelated features.
Even at 10,000 features, a standard correlation matrix is 50 million calculations, and is near untenable. At 1,000,000 features, almost all uncorrelated feature selection methods break down.
Not this one. In today's article we discuss a pretty cool feature selection method that removes all redundant features and still find the most predictive features.
As usual, retweet and comment for a free chance at a free article :-).
A historic day for all of us @TheJSWGroup - couldn’t be more proud of the team that has made this possible @JSWPaints. Grateful to God and to my family for supporting me and making this happen.
JSW Steel isn’t backing off. It just filed a review petition in India’s Supreme Court over the Bhushan Power deal collapse. A $2.3B takeover may hang in the balance. #JSWSteel#BhushanPower#SupremeCourt#IndiaInc#Insolvency#IBC
Read more https://t.co/27vmP6Cp2Z
I went to a mall and saw the @Happenstanceof1 store with a poster of Radhika Apte.
I bought footwear for me and my family. Good design should be paid for it helps your sole and soul.
#poetrycommunity#footwear#family
A heartfelt evening marking the new edition of Art India - where artist Thamshangpha Merci reflected on his connection of home in manipur through food https://t.co/oKQBuVJvew