Become a member
Sign in
Prateek Chhikara
Prateek Chhikara

Prateek Chhikara

3 Following
1 Followers
·
  • Profile
  • Claps
  • Highlights

Highlighted by Prateek Chhikara

See more

From Automatic feature extraction with t-SNE by Gonçalo Rodrigues

a into our favorite mod…atures or using Principal Component Analysis (PCA) before feeding the data into our favorite model. Even though PCA is amazing in most scenarios, it still is a linear model, which might not be powerful enough to apply to some datasets.

From Dimensionality Reduction by Mayur jain

By adding first 3 components, we have variance explained at 0.7 and by including 4th component we reach a variance of 0.8.So we can including 4 components instead of ten components thus reducing the dimension from 10 to 4.

From Dimensionality Reduction by Mayur jain

Consider we have set a threshold variance of 0.8, and if have ten components with a variance as follows 0.3, 0.25, 0.15, 0.1, 0.08, 0.08, 0.07, 0.07. then we can notice 0.3 is a component with maximum variance and is called as First Principal Component. Now since the threshold is kept at 0.8, we can add up components untill it reaches a variance of 0.8.

Claps from Prateek Chhikara

See more

text summarization: applications

Wenchen Li

Explaining Blockchain with Pokemon Cards

Luc Dossis

Ethereum tutorial #01 — What is a blockchain and its purpose?

Peter Ho