The code `y = le.fit_transform(labels)` performs the following: 1. **Context**: This...

The code y = le.fit_transform(labels) performs the following:

1. Context: This is most likely using the LabelEncoder class from sklearn.preprocessing, where le is an instance of the LabelEncoder. The labels variable is expected to be a list or array of categorical labels (e.g., strings or other non-numeric data).

2. What it does:

   • The .fit_transform() method of LabelEncoder both fits the encoder on the given labels and transforms them into integer-encoded numeric values.
   • Specifically:
     • fit: Determines the unique classes in labels and assigns each class a unique integer index in a consistent manner.
     • transform: Converts each label into its corresponding integer index based on the mapping learned during the fitting process.

3. Result:

   • The variable y will contain the numeric representation of labels, where each unique label has been mapped to a unique integer.

Example:

from sklearn.preprocessing import LabelEncoder

# Example labels
labels = ['cat', 'dog', 'fish', 'dog', 'cat']

# Create LabelEncoder instance
le = LabelEncoder()

# Fit and transform the labels
y = le.fit_transform(labels)

print(y)  # Output: [0 1 2 1 0]

In this case:

• 'cat' is encoded as 0
• 'dog' is encoded as 1
• 'fish' is encoded as 2.

This encoding is helpful when feeding non-numeric categorical data into machine learning models that require numeric input.