From the course: Mistakes to Avoid in Machine Learning
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Neglecting to consult subject matter experts
From the course: Mistakes to Avoid in Machine Learning
Neglecting to consult subject matter experts
- One of the defining traits of a great data scientist is the ability to create technically advanced solutions that have real world applicability. When we spend all our time heads down in the details, it can be difficult to make sure that what we're modeling is going to land and be a success in the business. That's where your subject matter experts or SME's come in. These SME our product managers or perhaps your customers in the business that you are creating this model for and they are likely the closest to the problem you're trying to solve. By setting up a conversation with your SME and soliciting feedback on a regular basis, you will drastically increase the likelihood of your model being successfully adopted. Here's a few of the reasons I want you to speak with and listen to your customers. First, they may uncover known issues with your data without you having to discover them in the exploratory data analysis phase.…
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Contents
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Assuming data is good to go2m 2s
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(Locked)
Neglecting to consult subject matter experts1m 48s
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Overfitting your models3m 25s
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Not standardizing your data2m 57s
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Focusing on the wrong factors2m 11s
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Data leakage2m 40s
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Forgetting traditional statistics tools1m 57s
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Assuming deployment is a breeze1m 47s
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Assuming machine learning is the answer1m 35s
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Developing in a silo2m 16s
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Not treating for imbalanced sampling3m 29s
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Interpreting your coefficients without properly treating for multicollinearity3m 19s
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Evaluating by accuracy alone6m 8s
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Giving overly technical presentations1m 56s
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