Credibility theory
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Factorization Machines for High Cardinality Features (Part 4 of 4)
This is the fourth in a 4-part series where Anders Larson and Shea Parkes discuss predictive analytics with high cardinality features. In the prior episodes we focused on approaches to handling individual high cardinality features, but these methods did not explicitly address feature interactions. Factorization Machines can responsibly estimate all pairwise interactions, even when multiple high cardinality features are included. With a healthy selection of high cardinality features, a well tuned Factorization Machine can produce results that are more accurate than any other learning algorithm. -
The Hashing Trick for High Cardinality Features (Part 3 of 4)
This is the third in a 4-part series where Anders Larson and Shea Parkes discuss predictive analytics with high cardinality features. In this episode they focus on feature engineering via the hashing trick. The hashing trick is most applicable for extremely high cardinality, and at first glance can seem almost ridiculous. In a lot of ways, it is the same as bucketing values at random. But there are times that it is more valuable to include randomly engineered buckets than to exclude the original high cardinality feature entirely. -
Y-aware Feature Engineering with High Cardinality Features (Part 2 of 4)
This is the second in a 4-part series where Anders Larson and Shea Parkes discuss predictive analytics with high cardinality features. In this episode they focus on y-aware feature engineering. Y-aware feature engineering is all about carefully bleeding information from your training response back into your engineered features without grossly misrepresenting your ability to generalize to new data. -
Introducing Predictive Analytics with High Cardinality Features (Part 1 of 4)
This is the first in a 4-part series where Anders Larson and Shea Parkes discuss predictive analytics with high cardinality features. In this episode they focus on introducing what high cardinality features are, why you might want to work with them and some of the basic approaches to including them in predictive models.
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