Just like we saw in CatBoost Part 1, Ordered Target Encoding, we're going to use the training data one row at a time to build and calculate the output values from trees. This is part of CatBoot's determined effort to avoid leakage like there is no tomorrow. We'll also learn how CatBoost makes predictions once the trees made.

NOTE: This StatQuest is based on the original CatBoost manuscript... https://arxiv.org/abs/1706.09516
...and an example provided in the CatBoost documentation...
https://catboost.ai/en/docs/concepts/...

English
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0:00 Awesome song and introduction
1:10 Building the first tree
6:05 Quantifying the effectiveness of the first threshold
6:56 Testing a second threshold
9:05 Building the second tree
10:21 The main idea of how CatBoost works
12:15 Making predictions
13:02 Symmetric Decision Trees
14:56 Summary of the main ideas

Corrections:
2:05 Red should have gone into bin 0 instead of bin 1.
7:23 I should have said that the cosine similarity was 0.71.

#StatQuest #CatBoost #DubbedWithAloud