6 Tricks I Learned From The OTTO Kaggle Challenge
Here are a few things I learned from the OTTO Group Kaggle competition. I had the chance to team up with great Kaggle Master Xavier Conort, and the french community as a whole has been very active. Hacking The Otto Group Challenge1 — Stacking, blending and averagingTeaming with Xavier has been the opportunity to practice some ensembling technics. We heavily used stacking. We added to an initial se
Supervised Classification with k-fold Cross Validation on a Multi Family Malware Dataset
Classifying a malware to a specific family is quite challenging with the growing number of malware and their families. Here, I will briefly describe how to do supervised classification on a multi-family malware dataset. Though this blog explains malware classification, the steps described below are quite generic for any multi-class classification. The dataset I will be using to demonstrate will be
GitHub - ycjuan/kaggle-2014-criteo
3 Idiots' Approach for Display Advertising Challenge ==================================================== This README introduces how to run our code up. For the introduction to our approach, please see http://www.csie.ntu.edu.tw/~r01922136/kaggle-2014-criteo.pdf LIBFFM ====== This most important model used in this solution is called ``field-aware factorization machines.'' If you want to use this m
ã„ã‹ã«ã—ã¦kaggleを解ãã‹ | threecourse's memo
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Keras vs Cancer
kaggle collection
Genetic Programming II - LB ~ 0.662
Kaggle Blog – Medium
How to Use Kaggle)
threecourse / kaggle-wiki / wiki / Past Competitions and Solutions (- June 2016) — Bitbucket
GitHub - kaz-Anova/ensemble_amazon: Code to share different ensemble techniques with focus on meta-stacking , using data from Amazon.com - Employee Access Challenge kaggle competition
Kaggle Blog – Medium
GitHub - benhamner/nips-2015-papers
Slack
Bosch Production Line Performance | Kaggle
Bosch Production Line Performance | Kaggle
Bosch Production Line Performance | Kaggle
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