Entries by tag: data-newsletter-4

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Lessons from the Netflix Prize: Changing Requirements and Cost-Effectiveness  03 Sep 2017    data-newsletter-4 recommender-systems
Netflix never really used the #1 winning solution to the Netflix Challenge. Some of the reasons were that just wasn't cost-effective to implement the full thing and another was that requirements had changed. Read More ›

Winning Solutions Overview: Kaggle Instacart Competition  03 Sep 2017    data-newsletter-4 kaggle data-science
The Instacart "Market Basket Analysis" competition focused on predicting repeated orders based upon past behaviour. Among the best-ranking solutings, there were many approaches based on gradient boosting and feature engineering and one approach based on end-to-end neural networks. Read More ›

A Quick Summary of Ensemble Learning Strategies  01 Sep 2017    data-newsletter-4 machine-learning
Ensemble learning refers to mixing the outputs of several classifiers in various ways, so as to get a better result than each classifier individually. Read More ›

Evaluation Metrics for Classification Problems: Quick Examples + References  31 Aug 2017    data-newsletter-4 machine-learning
There are multiple ways to measure your model's performance in machine learning, depending upon what objectives you have in mind. Some of the most important are Accuracy, Precision, Recall, F1 and AUC. Read More ›

Pandas for Large Data  13 Aug 2017    data-newsletter-4 pandas performance
In order to successfully work with large data on Pandas, there are some ways to reduce memory usage and make sure you get good speed performance. Read More ›