Smart Data Collective

MARCH 13, 2020

Phase 4: Knowledge Discovery. Algorithms can also be tested to come up with ideal outcomes and possibilities. When these two elements are in harmony, there are fewer delays and less risk of data corruption. Finally, models are developed to explain the data. Make the Workflow Obvious and Apparent to Others.

Data Science 120

Data Science Key Performance Indicator Knowledge Discovery Visualization 120

The Unofficial Google Data Science Blog

JULY 22, 2020

One reason to do ramp-up is to mitigate the risk of never before seen arms. A ramp-up strategy may mitigate the risk of upsetting the site’s loyal users who perhaps have strong preferences for the current statistics that are shown. For example, imagine a fantasy football site is considering displaying advanced player statistics.

Experimentation 96

Experimentation Statistics Testing Knowledge Discovery 96

AWS Big Data

SEPTEMBER 26, 2024

We recommend testing your use case and data with different models. The best way to determine the best parameters for a specific use case is to prototype and test. Test the solution In this demo, we can initiate the workflow by uploading documents to the raw prefix.

Data Lake 101

Data Lake Cost-Benefit Unstructured Data Modeling 101

Domino Data Lab

MAY 20, 2021

Their tests are performed using C4.5-generated This carries the risk of this modification performing worse than simpler approaches like majority under-sampling. note that this variant “performs worse than plain under-sampling based on AUC” when tested on the Adult dataset (Dua & Graff, 2017). Chawla et al., Chawla et al.

Machine Learning 136

Machine Learning Metrics Data mining Knowledge Discovery 136

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

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 Measurement 40

Domino Data Lab

AUGUST 1, 2021

This dataset classifies customers based on a set of attributes into two credit risk groups – good or bad. After forming the X and y variables, we split the data into training and test sets. This is to be expected, as there is no reason for a perfect 50:50 separation of the good vs. bad credit risk. show_in_notebook().

Modeling 139

Modeling Deep Learning Machine Learning Knowledge Discovery 139