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Note: this operation is quite long for both sets since we are dealing with quite a lot of events. Skip the following two code cells and trust us in using the high-level features for building your ML models! Indeed, we will obtain better discriminators' performance using high-level features. You can always return to this part of the exercise and try to use the low-level features.
# It will take a while (5 minutes), you can skip it as said before. # We leave you the output of this code cell using a .png format # VAR = [ 'f_massjj', 'f_deltajj', 'f_mass4l', 'f_Z1mass' , 'f_Z2mass', 'isSignal'] # sns.pairplot( data=df_all.filter(VAR), hue='isSignal' , kind='scatter', diag_kind='auto' );