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In Machine Learning, not all the data you collect is useful for analysis. In this video, you will learn about Feature Selection. You will understand the need for feature selection and what is feature selection. You will look at the various feature selection methods and get an idea about feature selection statistics. Finally, you will learn how to select features using a dataset and perform analysis in Python.
00:00:00 What’s in it for you
00:00:32 Need for feature selection
00:01:53 What is Feature selection
00:02:55 Feature selection method
00:07:16 Feature selection stats
00:08:06 Demo
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The file named Kobe Bryant remembered me of his demise😢😢
How can I classify features based on gain ratio?
This video was great as it provided a demo of how one of the methods works while selecting features.
I see that the features you were left within your demo were a combination of categorical and numeric/continuous features.
I have a couple of questions on this point;
a) Will you be applying any further feature selection techniques to the features you had remaining following the filter method demo so that you have the most relevant features that have a high importance score and/or correlation with the output variable before passing them through a few ML classifiers?
b) What are the feature selection methods used for supervised learning (binary classification) problem where the input features consists of categorical and numeric/continuous data and the class label is either positive-negative and/or 0-1?
Chi squared is pronounced – “Ky squared”. Informative video!
Great Video! Are there any drawbacks in using Feature Selection?
Minutes remaining may be in the period. Seconds remaining may be the shot clock. Maybe shouldn't be added together. Each would provide a different game constraint on the shooter and different information. But good video overall.
Please share ppt 🥹
Can you send me the Kobe_bryant.csv file?