Smart Data Collective

MARCH 13, 2020

But if you’re still working with outdated methods, you need to look for ways to fully optimize your approach as you move forward. Phase 4: Knowledge Discovery. Algorithms can also be tested to come up with ideal outcomes and possibilities. 5 Tips for Better Data Science Workflows. Take a 3D rendering project, for example.

Data Science 100

Data Science Key Performance Indicator Knowledge Discovery Visualization 100

The Unofficial Google Data Science Blog

OCTOBER 7, 2015

A/B testing is used widely in information technology companies to guide product development and improvements. For questions as disparate as website design and UI, prediction algorithms, or user flows within apps, live traffic tests help developers understand what works well for users and the business, and what doesn’t.

Modeling 61

Modeling Experimentation Knowledge Discovery Testing 61

Ontotext

APRIL 4, 2019

Milena Yankova : Our work is focused on helping companies make sense of their own knowledge. Within a large enterprise, there is a huge amount of data accumulated over the years – many decisions have been made and different methods have been tested. Some of this knowledge is locked and the company cannot access it.

Recreation/Entertainment 76

Recreation/Entertainment Testing Enterprise Knowledge Discovery 76

The Unofficial Google Data Science Blog

JULY 22, 2020

Another reason to use ramp-up is to test if a website's infrastructure can handle deploying a new arm to all of its users. The website wants to make sure they have the infrastructure to handle the feature while testing if engagement increases enough to justify the infrastructure. We offer two examples where this may be the case.

Experimentation 95

Experimentation Statistics Testing Strategy 95

The Unofficial Google Data Science Blog

NOVEMBER 4, 2015

One way to check $f_theta$ is to gather test data and check whether the model fits the relationship between training and test data. This tests the model’s ability to distinguish what is common for each item between the two data sets (the underlying $theta$) and what is different (the draw from $f_theta$).

KDD 40

KDD Testing Machine Learning Modeling 40

Domino Data Lab

AUGUST 1, 2021

After forming the X and y variables, we split the data into training and test sets. Next, we pick a sample that we want to get an explanation for, say the first sample from our test dataset (sample id 0). For sample 23 from the test set, the model is leaning towards a bad credit prediction. show_in_notebook(). Ribeiro, M.

Modeling 139

Modeling Deep Learning Machine Learning Knowledge Discovery 139