Occam's Razor

APRIL 8, 2013

To win in business you need to follow this process: Metrics > Hypothesis > Experiment > Act. We are far too enamored with data collection and reporting the standard metrics we love because others love them because someone else said they were nice so many years ago. That metric is tied to a KPI.

Metrics 156

Metrics KPI Analytics Key Performance Indicator 156

Peter James Thomas

AUGUST 12, 2018

Pertinence and fidelity of metrics developed from Data. Metrics are seldom reliant on just one data element, but are often rather combinations. There are often compromises to be made in defining metrics. Integrity of statistical estimates based on Data. For example: Is this a good way to define New Business Growth?

Metrics 49

Metrics Statistics Data Quality Measurement 49

The Unofficial Google Data Science Blog

NOVEMBER 17, 2020

On the one hand, basic statistical models (e.g. TF Lattice offers semantic regularizers that can be applied to models of varying complexity, from simple Generalized Additive Models, to flexible fully interacting models called lattices, to deep models that mix in arbitrary TF and Keras layers. monotonicity, diminishing returns).

Machine Learning 108

Machine Learning Deep Learning Modeling Interactive 108

Domino Data Lab

MAY 8, 2019

If your “performance” metrics are focused on predictive power, then you’ll probably end up with more complex models, and consequently less interpretable ones. They also require advanced skills in statistics, experimental design, causal inference, and so on – more than most data science teams will have.

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105