On Day 23 we dove into the deep end to understand why the error correction we used worked as well as it did. We showed how traditional machine learning uses loss functions and then hypothesized how our use helped improve predictions through its effe...

This post introduces new Python and R functionality how to get a quick summary of any model.

I’ve been spending time exploring Ningchuan Xiao’s excellent book, GIS Algorithms. This text offers a comprehensive dive into fundamental geospatial concepts including geometric algorithms, spatial indexing, and spatial analysis. The author thor...

1v1 LOL is a popular browser-based building and shooting game that attracts millions of players worldwide. The game typically runs on official platforms, but players often use 1v1 lol github repositories as another way to access it. These repositories have become valuable resources that help players enjoy the game in ...

On Day 22 we saw a meaningful improvement in our strategy by waiting an additional week to quantify model error and then using that error term to adjust the prediction on the most recently completed week of data. What was even more dramatic was comp...

One of the most exciting skills to learn in BI tools like Power BI or Tableau is how to create dynamic measures. The idea is that since dashboard space is limited, using dynamic controls—such as allowing users to select which measure to display—keeps the dashboard both compact and ...

On Day 21, we wrung our hands with frustration over how to proceed. The results of our circular block sampling suggested we shouldn’t expect a whole lot of outperformance in our 12-by-12 model out-of-sample. To deal with this our choices were, back ...

BigQuery ML has revolutionised how data scientists build machine learning models in data warehouses. BQML linear regression helps create accurate predictions right where your data lives. You can use SQL to build models that are just as reliable as traditional methods but without the usual complexity. Linear regression is one ...

GLMNet in Python: Generalized Linear Model, within the Techtonique ecosystem (nnetsauce, mlsauce)

On Day 20 we completed our analysis of the 12-by-12 strategy using circular block sampling on the 3 and 7 blocks. We found the strategy did not outperform buy-and-hold on average and its frequency of outperformance was modest – in the 28-31% range –...