Modeling Predictive Modeling Testing 
Analytics Vidhya
FEBRUARY 25, 2023
Introduction Often while working on predictive modeling, it is a common observation that most of the time model has good accuracy for the training data and lesser accuracy for the test data.
O'Reilly on Data
DECEMBER 12, 2019
Not least is the broadening realization that ML models can fail. And that’s why model debugging, the art and science of understanding and fixing problems in ML models, is so critical to the future of ML. Because all ML models make mistakes, everyone who cares about ML should also care about model debugging. [1]
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KDnuggets
AUGUST 31, 2022
When building a predictive model, the quality of the results depends on the data you use. In order to do so, you need to understand the difference between training and testing data in machine learning.
O'Reilly on Data
JANUARY 17, 2023
Building Models. A common task for a data scientist is to build a predictive model. You’ll try this with a few other algorithms, and their respective tuning parameters–maybe even break out TensorFlow to build a custom neural net along the way–and the winning model will be the one that heads to production.
O'Reilly on Data
SEPTEMBER 19, 2023
Stage 2: Machine learning models Hadoop could kind of do ML, thanks to third-party tools. While data scientists were no longer handling Hadoop-sized workloads, they were trying to build predictive models on a different kind of “large” dataset: so-called “unstructured data.” And it was good.
CIO Business Intelligence
FEBRUARY 6, 2025
The hype around large language models (LLMs) is undeniable. Think about it: LLMs like GPT-3 are incredibly complex deep learning models trained on massive datasets. Even basic predictive modeling can be done with lightweight machine learning in Python or R. They leverage around 15 different models.
O'Reilly on Data
MAY 18, 2020
This article answers these questions, based on our combined experience as both a lawyer and a data scientist responding to cybersecurity incidents, crafting legal frameworks to manage the risks of AI, and building sophisticated interpretable models to mitigate risk. All predictive models are wrong at times?—just
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