Hyperparameter tuning and interpretation of LSBoost output.

"R Python" continued... Random forests with monotonic constraints

We’re taking a short break from neural networks to return to portfolio optimization. Our last posts in the portfolio series discussed risk-constrained optimization. Before that we examined satisificing vs. mean-variance optimization (MVO). In our last post on that topic, we simulated 1,000 60-month (5-year) return series using the 1987-1991 period ...

This blog post was written by our intern Osheen Macoscar. 2020 is a year most of us would like to leave behind. But not all change is bad, and many interesting developments, especially in educatio...

Cold water cascades over the rocks in Erwin, Tennessee. Friends, this is going to be an epic post! Today, we bring together all the ideas we’ve built up over the past few posts to nail down our understanding of the key ideas in Jerome Friedma...

Introduction Over the past while with my time on LinkedIn, I got to have exposure to many people from many different lines of work. I also managed to have carved a space for myself there where I can post about Data Science topics and share my blogs along the way. ...

Open a bag of garden variety seeds and take a close look. What do you see? Is it a healthy seed? A broken seed? Could you accurately predict if that seed will grow? That’s what the Świeżewski lab sought to answer using Computer Vision models. And as it ...

Could you look at a seed and predict if it will grow? That’s just what the Świeżewski lab sought to answer and ultimately has achieved by identifying morphological parameters of thale cress (Arabidopsis thaliana) seeds and applying this discovery to Computer Vision models. By using a CNN for ...