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Arunansu Pattanayak
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Data Science Techniques to eliminate False Negatives

Started this discussion. Last reply by Arunansu Pattanayak Oct 28. 6 Replies

Quite often in Data Science we deal with use cases where the penalty for False Negative can be huge. Examples can be missing to predict having a disease, denying loan based on false prediction, not…Continue

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Latest Activity

Arunansu Pattanayak replied to Arunansu Pattanayak's discussion Data Science Techniques to eliminate False Negatives
"Thanks Kirk"
Oct 28
Kirk Borne replied to Arunansu Pattanayak's discussion Data Science Techniques to eliminate False Negatives
"Remember that a False Positive (FP) or False Negative (FN) is essentially caused by the output variable (the classification) overlapping in low-dimension feature space of input variables. Thus, one way to reduce FP and FN is to introduce higher…"
Oct 26
Arunansu Pattanayak replied to Arunansu Pattanayak's discussion Data Science Techniques to eliminate False Negatives
"Thanks Adil"
Oct 26
Arunansu Pattanayak replied to Arunansu Pattanayak's discussion Data Science Techniques to eliminate False Negatives
"How do those links relate to the topic?"
Oct 26
Adil Gursel Karacor replied to Arunansu Pattanayak's discussion Data Science Techniques to eliminate False Negatives
"This is not just about minimizing false negatives, but rather dealing with imbalanced classes. Some techniques to deal with this are: - use the right evalualtion metrics - basically do not just use accuracy as it is rather deceiving for the…"
Oct 24
Marissa Stice replied to Arunansu Pattanayak's discussion Data Science Techniques to eliminate False Negatives
Oct 23
Arunansu Pattanayak posted a discussion

Data Science Techniques to eliminate False Negatives

Quite often in Data Science we deal with use cases where the penalty for False Negative can be huge. Examples can be missing to predict having a disease, denying loan based on false prediction, not acting on a cyber threat etc. So the question is what kind of techniques exist to minimize or eliminate false negatives?See More
Oct 22
Arunansu Pattanayak's 2 discussions were featured
Oct 22

Profile Information

Company:
Microsoft
Job Title:
Cloud Solution Architect
Seniority:
Architect
Job Function:
Data Scientist
Country
USA
Number of employees:
100.000+
Industry:
Technology
LinkedIn Profile:
http://https://www.linkedin.com/in/arunansu/
Interests:
Contributing, Networking
Topics of Interest
Data Science, Machine Learning, Deep Learning, Data Visualization, Data Storytelling, Enterprise Data Modeling, Graph Technology, Fintech, Artificial General Intelligence, Neural Networks

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