Partial Dependence Plots: How to Discover Variables Influencing a Modelby@mythilikrishnan
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Partial Dependence Plots: How to Discover Variables Influencing a Model

by Mythili Krishnan5mJanuary 13th, 2021
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We will use the FIFA 2018 dataset on Kaggle and explore the following models: Decision Tree model and Random Forest model. Explore the most influential variables in both the models and how they are affecting the accuracy. Find the threshold of the influential variables and how much they influence the accuracy of the models. Use the data and the libraries to train the decision tree model and then train the random forest model. The random forest has a better accuracy at 71.88% with (10+13) targets identified correctly and (6+3) targets mis-classified-6 being false positives and 3 being false negatives respectively.

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Mythili Krishnan

Mythili Krishnan

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Data science leader and regular author in "Towards Datascience' medium publication

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Mythili Krishnan@mythilikrishnan
Data science leader and regular author in "Towards Datascience' medium publication

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