Machine Learning Project - Feature Selection and Extraction

YouTube Excerpt: Feature selection in machine learning is the selecting of most relevant features in a model. Detecting the subset of relevant features in order to obtain simple model, for better interpretation, shorter training time and enhance performance. Playlist : https://www.youtube.com/watch?v=AOKQG9NL4qo&list=PLjDarHRb40ZNUdgAPBrAwjSqW2Y01cIF4 Feature extraction in machine learning involves finding and getting important features from data. Beyond the Bag of Words model, the term frequency/inverse document frequency (TF/IDF) model learns a vocabulary from all of the documents, then models each document by calculating a numerical statistic for each word of the document that reflects how important the word is tothe document. Methods Used In Feature Extraction A. TfidfVectorizer B. CountVectorizer C. DictVectorizer #machinelearning #featureextraction #featureselection #Machinelearningproject #TfidfVectorizer #CountVectorizer #DictVectorizer

Feature selection in machine learning is the selecting of most relevant features in a model. Detecting the subset of relevant features in order to...

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