Can we use PCA for feature selection | Data Science Interview Questions Podcast

preview_player
Показать описание
Can we use (Principal Component Analysis) PCA for feature selection | Data Science Interview Questions Podcast

-----------------

----------------

You can find me here:

**********************************************

**********************************************

Other Playlist you might like 👇

#machinelearninginterview #interviewprep #interviewquestions #huggingface
#naturallanguageprocessing #transformers #machinelearning #datascience #nlp #textprocessing #kaggle #tensorflow #pytorch #deeplearning #deeplearningai #100daysofmlcode #neuralnetworks #pythonprogramming #python #100DaysOfMLCode #softwareengineer #dataanalysis #machinelearningalgorithms #computervision #coding #bigdata #computerscience #tech #data #iot #software #dataanalytics #programmer #ml #coder #analytics
Рекомендации по теме
Комментарии
Автор

such video format that addresses a specific point is very helpful and interesting.

wryltxw
Автор

curious to see an example of how exactly it can help in feature selection process. do you mean if there are two principle components that explains 99% of the variance, then you would choose two features among the many? even though the components are linear combinations of the original features.

wryltxw