Machine Learning Tutorial Chap 7 | Part-3 Feature Selection | Rohit Ghosh | GreyAtom
Channel: GreyAtom EduTech
Date: 28/06/2019

Welcome to the #DataScienceFridays Rohit Ghosh, a deep learning scientist and an Instructor at GreyAtom will take us through Feature Selection in machine learning through this introduction series. 

In machine learning and statistics, feature selection, also known as variable selection, attribute selection or variable subset selection, is the process of selecting a subset of relevant features (variables, predictors) for use in model construction. Feature selection techniques are used for four reasons:
simplification of models to make them easier to interpret by researchers/users, - 
shorter training times,
to avoid the curse of dimensionality,
enhanced generalization by reducing overfitting(formally, reduction of variance)

The central premise when using a feature selection technique is that the data contains some features that are either redundant or irrelevant, and can thus be removed without incurring much loss of information. Redundant and irrelevant are two distinct notions, since one relevant feature may be redundant in the presence of another relevant feature with which it is strongly correlated.

Feature selection techniques should be distinguished from feature extraction. Feature extraction creates new features from functions of the original features, whereas feature selection returns a subset of the features. Feature selection techniques are often used in domains where there are many features and comparatively few samples




This is the 3rd in 3 videos about Feature Selection in Machine Learning. 

In this video, we will explore the Embedded Method.

Embedded methods have been recently proposed that try to combine the advantages of both previous methods. A learning algorithm takes advantage of its own variable selection process and performs feature selection and classification simultaneously. 



Complete Playlist for the Course: https://bit.ly/2Q1zvK6 

After completing our 3-part Series on Feature Selection in Machine Learning, you will be able to do the following:

Apply Univariate feature selection techniques
Apply Bivariate feature selection techniques
Understand correlation and causation
Learn how to use sklearn for feature selection


Here’s the full syllabus of our 3-part video on Feature Selection in Machine Learning:


Feature Selection & its importance
Why to use feature selection
Sklearn for feature selection
Types of Feature Selection - Filter Method - Wrapper Method - Embedded Method 
Difference between filter and wrapper methods

#machinelearningtutorial #featureselection #DataScience101 #Greyatom

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