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How to read feature importance weights for Linear Regression
Let’s get a simplistic view of the output of feature weights for the house pricing dataset. The top 3 most important linear regression features when training to predict a house price are year=2018, location=USA and age.
year_2018 is a categorical variable. The weight 0.3145 tells us that the average price of a house is higher by 0.3145 units when the years is 2018 compared to 2017 and 2019 given all other variables are constant.
age is a continuous variable. The weight 0.0488 tells us that a unit increase in age (e.g. when the person is one year older) results in an average increase of 0.0488 of the house price.
Weight Feature 0.3145 year_2018 0.1503 location_USA 0.0488 age 0.0312 year_2017 0.0057 interest .... ....
This post is a follow up to the Feature Importance with OneHotEncoder and Pipelines in Scikit-learn.
Another related post is the Regression Tips anc Tricks Series I have made.
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