EmbRaceR- Feature Selection R

$159.95

  • EUR: €136.71
  • GBP: £117.46
  • NZD: $228.47
  • CAD: $204.22
  • AUD: $220.88

Too many variables in a model can make its own problem. This is the fifth course in the EmbRaceR course set and shows how to do feature selection, including introducing the basics of matrix calculations.

Length: 59 minutes

Description

EmbRaceR- Feature Selection R

Too many variables in a model can make its own problem. This is the fifth course in the EmbRaceR course set and shows how to do feature selection, including introducing the basics of matrix calculations.

Many methods evolved for optimizing the number of variables in linear models. You will learn about forward and backward stepwise regression, best subsets regression, and then about ridge, lasso, and elastic net regularization methods. The first module also introduces basic matrix operations. This lesson is a preparation for the second module.

Principal component analysis (PCA) stems from matrix algebra and is probably the most widely used feature selection method. PCA is also useful for anomaly detection. Very similar to PCA is exploratory factor analysis (EFA), where you try to find hidden latent variables, called factors, that might be more important to use in further analysis that the basic variables, and which also give you more in-depth understanding of the problem you are analyzing.

Link to the agenda and sample videos from the course

 

Length: 59 minutes

 

Instructor

Dejan Sarka


Dejan Sarka, MCT and Data Platform MVP, is an independent trainer and consultant that focuses on the development of database and business intelligence applications. Besides projects, he spends about half of the time on training and mentoring. He is the founder of the Slovenian SQL Server and .NET Users Group. Dejan Sarka is the main author or co-author of eighteen books about databases and SQL Server. Dejan Sarka has also developed many courses and seminars for Microsoft, SolidQ, and Pluralsight.

 

 

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