Occam's Razor

APRIL 8, 2013

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. Sometimes, we escape the clutches of this sub optimal existence and do pick good metrics or engage in simple A/B testing. Testing out a new feature.

Metrics 156

Metrics KPI Analytics Key Performance Indicator 156

Domino Data Lab

MAY 8, 2019

The lens of reductionism and an overemphasis on engineering becomes an Achilles heel for data science work. Instead, consider a “full stack” tracing from the point of data collection all the way out through inference. data collection”) show the “process” steps that a team performs, while the boxes (e.g.,

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105

Occam's Razor

FEBRUARY 22, 2010

It is simply magnificent what you can do with freely available data on the web about your direct competitors, your industry segment and indeed how people behave on search engines and other websites. Not all sources of CI data are created equal. Typically, data collected is anonymous and not personally identifiable information (PII).

Metrics 123

Metrics Advertising Reporting Software 123

datapine

JANUARY 31, 2022

The companies that are most successful at marketing in both B2C and B2B are using data and online BI tools to craft hyper-specific campaigns that reach out to targeted prospects with a curated message. Everything is being tested, and then the campaigns that succeed get more money put into them, while the others aren’t repeated.

Business Intelligence 214

Business Intelligence Analytics Cost-Benefit ROI 214

Domino Data Lab

AUGUST 1, 2021

After forming the X and y variables, we split the data into training and test sets. Looking at the target vector in the training subset, we notice that our training data is highly imbalanced. PDPs for the bicycle count prediction model (Molnar, 2009). X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2,

Modeling 139

Modeling Deep Learning Machine Learning Knowledge Discovery 139