Feature Selection methods with example (Variable selection methods)_2.pdf - Enrollments Open | New Certi ed AI and ML BlackBelt Program Enroll

Feature Selection methods with example (Variable selection methods)_2.pdf

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Enrollments Open | New Certi±ed AI and ML BlackBelt+ Program - Enroll Now (? utm_source=blog&utm_medium=²ashstrip&utm_campaign=bbplus) LOGIN / REGISTER (? NEXT=- HOW-TO-SELECT-THE-RIGHT-VARIABLES/) (- source=blog&utm-medium=top-icon/) (? utm_source=blog&utm_medium=top_banner_blog&utm_campaign=bbplus) Introduction to Feature Selection methods with an example (or how to select the right variables?) CLASSIFICATION () INTERMEDIATE () MACHINE LEARNING () PYTHON () R () STRUCTURED DATA () SUPERVISED () TECHNIQUE ()
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SAURAV KAUSHIK (), DECEMBER 1, 2016 LOGIN TO BOOKMARK… (? utm_source=blog&utm_medium=bannerbelowtitle&utm_campaign=7_march_applicationsclosing) Introduction One of the best ways I use to learn machine learning (- to-data-science-2?utm_source=blog&utm_medium=IntroductiontoFeatureSelectionarticle) is by benchmarking myself against the best data scientists in competitions. It gives you a lot of insight into how you perform against the best on a level playing ±eld. Initially, I used to believe that machine learning is going to be all about algorithms – know which one to apply when and you will come on the top. When I got there, I realized that was not the case – the winners were using the same algorithms which a lot of other people were using. Next, I thought surely these people would have better / superior machines. I discovered that is not the case. I saw competitions being won using a MacBook Air, which is not the best computational machine. Over time, I realized that there are 2 things which distinguish winners from others in most of the cases: Feature Creation and Feature Selection . In other words, it boils down to creating variables which capture hidden business insights and then making the right choices about which variable to choose for your predictive models (?
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