Remove 2013 Remove Modeling Remove Predictive Modeling Remove Statistics
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Why you should care about debugging machine learning models

O'Reilly on Data

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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Credit Card Fraud Detection using XGBoost, SMOTE, and threshold moving

Domino Data Lab

We’ll use a gradient boosting technique via XGBoost to create a model and I’ll walk you through steps you can take to avoid overfitting and build a model that is fit for purpose and ready for production. Let’s also look at the basic descriptive statistics for all attributes. from sklearn import metrics.

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Deep Learning Illustrated: Building Natural Language Processing Models

Domino Data Lab

The excerpt covers how to create word vectors and utilize them as an input into a deep learning model. While the field of computational linguistics, or Natural Language Processing (NLP), has been around for decades, the increased interest in and use of deep learning models has also propelled applications of NLP forward within industry.

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Data Science at The New York Times

Domino Data Lab

Assessing whether a business stakeholder is trying to solve for a problem that is descriptive, predictive, or prescriptive and then re-framing the problem as supervised learning, unsupervised learning, or reinforcement learning, respectively. One of the ways I frame that is, “Are you looking to build a predictive model?

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What Is Embedded Analytics?

Jet Global

Companies like Tableau (which raised over $250 million when it had its IPO in 2013) demonstrated an unmet need in the market. These licensing terms are critical: Perpetual license vs subscription: Subscription is a pay-as-you-go model that provides flexibility as you evaluate a vendor. Their dashboards were visually stunning.