Feature Enigneering 공부

• Feature Engineering - Wikipedia
• Discover Feature Engineering, How to Engineer Features and How to Get Good at It - MachineLeariningMastery
• Feature Engineering Using Pandas for Beginners - Analytics Vidhya
• GroupBy in Pandas: Your Guide to Summarizing and Aggregating Data in Python - Analytics Vidhya
• feature templates - Stanford cs221

https://medium.com/@rishabhrjain/linear-regrsesion-binning-and-polynomial-linear-regression-3ed62f79ce0

https://towardsdatascience.com/feature-engineering-deep-dive-into-encoding-and-binning-techniques-5618d55a6b38

출처: https://towardsdatascience.com/feature-engineering-deep-dive-into-encoding-and-binning-techniques-5618d55a6b38

 

Feature Engineering — deep dive into Encoding and Binning techniques

https://medium.com/machine-learning-researcher/dimensionality-reduction-pca-and-lda-6be91734f567

Dimensionality Reduction(PCA and LDA)

밑의 글은 모두 출처가 위의 글입니다. 

 

Reduce the dimension of your feature space

-> Feature Elimination / Feature extraction (PCA)

 

* PCA : drop the "least important" variables

When should I use PCA?
1. Do you want to reduce the no. of variables, but are not able to identify variables to completely remove from consideration?
2. Do you want to ensure your variables are independent of one another?
3. Are you comfortable making your independent variable less interpretable?

 

아...어떡하지 수학 나와서 읽기 싫다. 하필 eigenvalues 나오네...잊어먹었는데ㅔ...