Cole Brendel – Medium
Invest in New Data before New Models
How better data outperforms better models
Suppose you are working on a classification problem, and you think you’ve hit a wall when it comes to model performance. What should you do? I suspect that the ever curious researcher lurking in all of us would think to explore new model algorithms first. …
In data science we are often asked question which require causal answers. For example, did the updated version of the app increase usage? In an ideal world, we would be able to run an A/B test; however, we find ourselves in a world that is not always ideal. …
Moving beyond K-Means into Hierarchical Clustering
Clustering analyses allow one to identify and interpret structure within a data set. As data scientists, we are often asked questions which can be answered only by understanding the latent structure within our data. For example, the marketing team might want to understand the “categories” of users that are using…
3 poems about the backyard
Commanding my helm
Surveying my kingdom below
The patties exist as humble circles
Beckon, shift into the third dimension
Like so many meatballs in genuflection
Golden sun kisses the leaves…
How to train and evaluate multiple models efficiently
A question all data scientists must confront when working on any machine learning project is…
Which model architecture is going to best fit my data?
There are several theoretical considerations one should take into account. For example, if your features show strong linear relationships to your dependent variable (target) then…
Configure XGBoost Models with YAML
How to use YAML files to make your models rapidly sharable, wickedly flexible, and to prepare them for production
There is nothing more enjoyable than starting work on a new ML project. First, you get your hands dirty with some data cleaning, then you get to load that squeaky-clean data…
Cluster-then-predict for classification tasks
How to leverage unsupervised learning in your supervised learning problems
Supervised classification problems require a dataset with (a) a categorical dependent variable (the “target variable”) and (b) a set of independent variables (“features”) which may (or may not!) be useful in predicting the class. The modeling task is to learn a function mapping features and their values to a target…