Class Imbalance

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So if you’re attempting to predict, let’s say, cancer, your data must have a fair balance between positive cancer results and negative results.  If your data has 10 positive results and a million negatives, you will probably not be able to form a useful algorithm.

Luckily, I found this little function that will go through your data and give you the balance in your data.

def print_dx_perc(data_frame, col):
   dx_vals = data_frame[col].value_counts()
   dx_vals = dx_vals.reset_index()
   f = lambda x, y: 100 * (x / sum(y))
   for i in range(0, len(dx)):
      print('{0} accounts for {1:.2f}% of the diagnosis class'.format(dx[i], f(dx_vals[col].iloc[i],
         dx_vals[col])))

print_dx_perc(breast_cancer, 'diagnosis')

 

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