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Created 4 years ago
AI for Medicine Course 1 Week 1 lecture exercises
Counting labels
As you saw in the lecture videos, one way to avoid having class imbalance impact the loss function is to weight the losses differently. To choose the weights, you first need to calculate the class frequencies.
For this exercise, you'll just get the count of each label. Later on, you'll use the concepts practiced here to calculate frequencies in the assignment!
# Import the necessary packages
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline# Read csv file containing training datadata
train_df = pd.read_csv("nih/train-small.csv")# Count up the number of instances of each class (drop non-class columns from the counts)
class_counts = train_df.sum().drop(['Image','PatientId'])