I would like to visualize my features using the code below. However, I am getting an error that my features are being recognized as "nan" instead of their actual names.
Instead of the below
Feature = [and then writing the features that I want], I am assigning
feature = data [0,1:]. This is the first row of my data that has the features in it. I have many features that I don't want to write as string but would just like to extract from the data file directly. How can I do this and get the names of the features instead of 'nan'?
# Load the dataset data = load_data('credit') # Specify the features of interest features = [ 'limit', 'sex', 'edu', 'married', 'age', 'apr_delay', 'may_delay', 'jun_delay', 'jul_delay', 'aug_delay', 'sep_delay', 'apr_bill', 'may_bill', 'jun_bill', 'jul_bill', 'aug_bill', 'sep_bill', 'apr_pay', 'may_pay', 'jun_pay', 'jul_pay', 'aug_pay', 'sep_pay', ] # Extract the instances and target X = data[features] y = data.default from yellowbrick.features import Rank1D # Instantiate the 1D visualizer with the Sharpiro ranking algorithm visualizer = Rank1D(features=features, algorithm='shapiro') visualizer.fit(X, y) # Fit the data to the visualizer visualizer.transform(X) # Transform the data visualizer.poof() # Draw/show/poof the data