From the course: Data Science Foundations: Data Assessment for Predictive Modeling
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Techniques for working with the top predictors
From the course: Data Science Foundations: Data Assessment for Predictive Modeling
Techniques for working with the top predictors
- [Instructor] I'm in KNIME with an unmodified version of the census data set, and I've started a new workflow. What we're going to do now is walk through how to establish the relationships in your strong predictors to then discuss them with a subject matter expert. My favorite technique, is to grow the top branch of a decision tree. I'm going to take KNIME's Decision Tree Learner, and hook it up to the data, double click to configure it, and my class column is going to be income, we can keep it as that, but I'm going to go down here to force route split column, and what you want to do is grab one of the variables that you think has a strong relationship with this, and realize that at this point you would know that because you would have done by variate relationships of all the payers with the target variable. For instance, gender is going to have a strong relationship with income, and I can maximize that. Let's remember what…
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How to utilize an SME's time effectively2m 8s
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Techniques for working with the top predictors4m 19s
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Advice for weak predictors6m 4s
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Tips and tricks when searching for quirks in your data4m 46s
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Learning when to discard rows2m 5s
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Introducing ggplot21m 44s
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Orientating to R's ggplot2 for powerful multivariate data visualizations5m 52s
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Challenge: Producing multivariate visualizations for case study 11m 12s
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Solution: Producing multivariate visualizations for case study 12m 31s
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