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Kaggle Fundamentals: The Titanic Competition
Kaggle Fundamentals: The Titanic Competition - Added November 14, 2017 at 11:44AM
data-science  kaggle  read2of 
12 days ago by xenocid
kaggleメルカリコンペの表彰式イベントに参加してきました - 周回遅れでIT業界デビューしたエンジニアのブログ
“kaggleメルカリコンペの表彰式イベントに参加してきました - 周回遅れでIT業界デビューしたエンジニアのブログ”
kaggle  data_science  from twitter_favs
13 days ago by ocelot
GitHub - MLWave/Kaggle-Ensemble-Guide: Code for the Kaggle Ensembling Guide Article on MLWave
Code for the Kaggle Ensembling Guide Article on MLWave
A combination of Model Ensembling methods that is extremely useful for increasing accuracy of Kaggle's submission. For more information: http://mlwave.com/kaggle-ensembling-guide/
ensembling  kaggle  ml 
15 days ago by hellsten
Kaggle Ensembling Guide | MLWave
We will now show how these pseudo-classifiers are able to obtain 78% accuracy through a voting ensemble.

A pinch of maths
For a majority vote with 3 members we can expect 4 outcomes:

All three are correct
0.7 * 0.7 * 0.7
= 0.3429

Two are correct
0.7 * 0.7 * 0.3
+ 0.7 * 0.3 * 0.7
+ 0.3 * 0.7 * 0.7
= 0.4409

Two are wrong
0.3 * 0.3 * 0.7
+ 0.3 * 0.7 * 0.3
+ 0.7 * 0.3 * 0.3
= 0.189

All three are wrong
0.3 * 0.3 * 0.3
= 0.027
We see that most of the times (~44%) the majority vote corrects an error. This majority vote ensemble will be correct an average of ~78% (0.3429 + 0.4409 = 0.7838).
kaggle  ensembling  correlation  ml  ai 
15 days ago by hellsten
Datasets | Kaggle
The best place to discover and seamlessly analyze open data
datasets  kaggle  opendata  data  open 
8 weeks ago by wjy

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