- Working With Sparse Features In Machine Learning Models - Jan 12, 2021.
Sparse features can cause problems like overfitting and suboptimal results in learning models, and understanding why this happens is crucial when developing models. Multiple methods, including dimensionality reduction, are available to overcome issues due to sparse features.
Data Preparation, Feature Engineering, Machine Learning, Overfitting, Sparse data
- Sparse Matrix Representation in Python - May 19, 2020.
Leveraging sparse matrix representations for your data when appropriate can spare you memory storage. Have a look at the reasons why, see how to create sparse matrices in Python using Scipy, and compare the memory requirements for standard and sparse representations of the same data.
numpy, Python, scikit-learn, SciPy, Sparse data
- Interview: Ali Vanderveld, Groupon on Vital Ingredients of Analytics-powered Sales Force - Jul 16, 2015.
We discuss the role of Analytics at Groupon, deciding factors for merchant priority, limitations of historical data, optimizing the efforts of sales force, data characteristics and dealing with Data Sparsity.
Ali Vanderveld, Analytics, Challenges, Forecasting, Groupon, Interview, Marketing, Sales, Sparse data
- Evolution of Fraud Analytics – An Inside Story - Mar 14, 2014.
The amazing analytic innovations in payment fraud prevention can be grouped into three major categories: large data-set modeling, sparse data-set modeling, and false-positive reductions - a view from the inside.
False positive, FICO, Fraud analytics, Fraud Prevention, Neural Networks, Sparse data
