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

Occam's Razor

SEPTEMBER 29, 2009

Remember: Engagement is not a metric, its an excuse. ]. Ideally you'll measure the number prior to your branding campaign, say Feb 2009, and then you'll measure it again during your campaign, March 2009. The ideal metrics for this desired outcome are Visitor Loyalty & Visitor Recency. 7 Best Practices ].

Measurement 128

Measurement Metrics Analytics Marketing 128

Occam's Razor

MAY 14, 2012

Bonus One: Read: Brand Measurement: Analytics & Metrics for Branding Campaigns ]. There are many different tools, both online and offline, that measure the elusive metric called brand strength. They are full of specific insights you can use to optimize your online search campaigns. Amazon is an interesting example.

Measurement 128

Measurement Analytics Advertising Marketing 128

Domino Data Lab

APRIL 21, 2021

In contrast, the decision tree classifies observations based on attribute splits learned from the statistical properties of the training data. Machine Learning-based detection – using statistical learning is another approach that is gaining popularity, mostly because it is less laborious. from sklearn import metrics.

Statistics 69

Statistics Machine Learning Modeling Metrics 69

Occam's Razor

FEBRUARY 22, 2010

The secret to making optimal use of CI data lies in one single realization: You must ensure you understand how the data you are analyzing is collected. Check the definitions of various metrics. For example, if you see a metric called Cookies, find out exactly what that metric means before you use the data.

Metrics 123

Metrics Advertising Reporting Software 123

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

The Unofficial Google Data Science Blog

NOVEMBER 17, 2020

On the one hand, basic statistical models (e.g. The first is that they are straightforward to optimize using traditional gradient-based optimizers as long as we pre-specify the placement of the knots. There is a robust set of tools for working with these kinds of constrained optimization problems. References Wightman, L.

Machine Learning 108

Machine Learning Deep Learning Modeling Interactive 108