Tag - Let’s

5 Misconceptions About Data Science

Despite the massive advantages and benefits big data, machine learning and predictive analytics have to offer, data science is still a touchy subject for businesses of all sizes. Not only are many reluctant to adopt the related systems and hardware,...

Annotated Facets with ggplot2

(This article was first published on R – Stat Bandit, and kindly contributed to R-bloggers) I was recently asked to do a panel of grouped boxplots of a continuous variable, with each panel representing a categorical grouping variable. This seems easy...

Back to returns forecasting

Neural networks for algorithmic trading. Volatility forecasting and custom loss functions Hi again! In last three tutorials we compared different architectures for financial time series forecasting, realized how to do this forecasting adequately with...

Basic Concepts of Feature Selection

Feature selection is a key part of Sponsored Post. Why should we care about Feature Selection? There is a consensus that feature engineering often has a bigger impact on the quality of a model than the model type or its parameters. Feature selection...

Boyan Angelov

Working with Missing Data in Machine Learning Missing values are representative of the messiness of real world data. There can be a multitude of reasons why they occur — ranging from human errors during data entry, incorrect sensor readings, to softw...

Calculating AUC: the area under a ROC Curve

by Bob Horton, Microsoft Senior Data Scientist Receiver Operating Characteristic (ROC) curves are a popular way to visualize the tradeoffs between sensitivitiy and specificity in a binary classifier. In an earlier post, I described a simple “turtle’s...

Cards on the table

Share Tweet After the last post building on feedback from readers, the blog is back to the regular program of recycling old Github repos. Today’s project was waiting for its turn here and will involve a Catan card game. Nearly a year ago, I played Ca...

Chase Roberts

How to unit test machine learning code. Over the past year, I’ve spent most of my working time doing deep learning research and internships. And a lot of that year was making very big mistakes that helped me learn a lot about not just about ML, but a...