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The Difference Between Training and Testing Data in Machine Learning

KDnuggets

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.

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Adversarial Validation- Improving Ranking in Hackathon

Analytics Vidhya

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.

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Why you should care about debugging machine learning models

O'Reilly on Data

For all the excitement about machine learning (ML), there are serious impediments to its widespread adoption. 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.

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Of Muffins and Machine Learning Models

Cloudera

In this example, the Machine Learning (ML) model struggles to differentiate between a chihuahua and a muffin. Will the model correctly determine it is a muffin or get confused and think it is a chihuahua? The extent to which we can predict how the model will classify an image given a change input (e.g.

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Using Cloudera Machine Learning to Build a Predictive Maintenance Model for Jet Engines

Cloudera

Not many other industries have such a sophisticated business model that encompasses a culture of streamlined supply chains, predictive maintenance, and unwavering customer satisfaction. Step 1: Using the training data to create a model/classifier. Fig 2: Diagram showing how CML is used to build ML training models.

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12 data science certifications that will pay off

CIO Business Intelligence

The exam tests general knowledge of the platform and applies to multiple roles, including administrator, developer, data analyst, data engineer, data scientist, and system architect. Candidates for the exam are tested on ML, AI solutions, NLP, computer vision, and predictive analytics.

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How to Leverage Machine Learning for AML Compliance

BizAcuity

1] With the rise of Big Data in today’s world, Machine Learning (ML) is popularly used to identify, assess, and monitor financial risks as well as detect various suspicious activities and transactions. There are a number of open-source ML platforms like KNIME that can also be leveraged to detect and predict suspicious behavior.