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Meta-Learning For Better Machine Learning

Rocket-Powered Data Science

So, you start by assuming a value for k and making random assumptions about the cluster means, and then iterate until you find the optimal set of clusters, based upon some evaluation metric. There are several choices for such evaluation metrics: Dunn index, Davies-Bouldin index, C-index, and Silhouette analysis are just a few examples.

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9 Habits of Data Fluent Organizations — and How to Learn Them

Juice Analytics

With our book , resources and workshops, we’ve shared guidance about what it takes to become a data fluent organization. Habit 1: Define shared metrics Data fluency requires getting everyone on the same page as to what matters most. For difficult choices, we have shared baseline: How will it impact our North Star Metric?

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Build multi-Region resilient Apache Kafka applications with identical topic names using Amazon MSK and Amazon MSK Replicator

AWS Big Data

Some important MSK Replicator metrics to monitor are ReplicationLatency , MessageLag , and ReplicatorThroughput. To understand how many bytes are processed by MSK Replicator, you should monitor the metric ReplicatorBytesInPerSec. This metric indicates the average number of bytes processed by the replicator per second.

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12 Rules for Data Storytelling

Juice Analytics

Or maybe you don’t have the time to attend a world-class data storytelling workshop ? The choices you make — the metrics and visualization you choose, the sequence of content, even how you label the data — these are all an expression of your priorities and insights into the data. No problem. Part 1: Think Like a Storyteller.

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Why you should care about debugging machine learning models

O'Reilly on Data

For model training and selection, we recommend considering fairness metrics when selecting hyperparameters and decision cutoff thresholds. Last, for prediction post-processing, changing model predictions after training, like reject-option classification in AIF360 or Themis ML , can also help to reduce unwanted bias.

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Why Build a Data-Driven Culture?

Juice Analytics

When leaders throughout the organization show data-driven behavior, like incorporating key metrics into status meetings or championing a new dashboard, everyone will get the message. Ask us about our new workshop to kick-start your data communication skills and plan a path forward toward a data-driven culture. Modeling behavior.

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CDOs: Your AI is smart, but your ESG is dumb. Here’s how to fix it

CIO Business Intelligence

Developers, data architects and data engineers can initiate change at the grassroots level from integrating sustainability metrics into data models to ensuring ESG data integrity and fostering collaboration with sustainability teams. However, embedding ESG into an enterprise data strategy doesnt have to start as a C-suite directive.

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