# Feature Engineering; Fast Randomized SVD; Visualizing Percentage Change

May 13, 2018 · 2 min read

Issue #4: 2018/05/07 to 2018/05/13

This is an experimental series in which I briefly introduce the interesting data science stuffs I read, watched, or listened to during the week. Please give this post some claps if you’d like this series to be continued.

# Feature Engineering

Someone brought up this set of slides by Dmitry Larko when talking about “weight of evidence” encoding in Kaggle TalkingData AdTracking Fraud Detection Challenge:

It’s a very good resource especially when you ran out of feature engineering ideas.

I ran into this video of a talk given a while ago. Rachel Thomas used fast randomized SVD as an example to show us how adding randomness can greatly improve performance of a program that does not require too much precision. Also motivated me to refresh my knowledge of SVD and general linear algebra.

# Mike Bostock on Visualizing Percentage Change

Here’s one way to understand the logic — if a quantity went down by 50%, to go back to the previous level, it requires a 100% increase instead of 50% increase. So in this sense, -50% is as big as +100%.

Or as Mr. Bostock put it, (as an example) comparing 200% and -200% makes no sense when counting people.

# Fast.ai 2018 DL Course Notes by Hiromi Suenaga

I’ve started doing a speed run through the new Fast.ai (based on PyTorch) course (Part 1) this week. Coincidentally, Part 2 was also officially launched on May 7th. So the timing couldn’t be better.

Hiromi Suenaga has shared her detailed course notes. It’s a bit hard to go between lessons, so here’s a quick list:

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## An Example of Hyperparameter Optimization on XGBoost, LightGBM and CatBoost using Hyperopt

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