The code creates a LabelEncoder instance, fits it to the 'label' column of the train dataset, and uses it to encode categorical labels into numerical values. This is a typical preprocessing step in machine learning pipelines.
le = LabelEncoder()
le.fit(train['label'])```markdown
# Label Encoding
### Transform categorical 'label' column in train dataset
#### Utilize LabelEncoder from sklearn.preprocessing
from sklearn.preprocessing import LabelEncoder
import pandas as pd # Import pandas for DataFrame operations
def label_encode_train_data(train: pd.DataFrame) -> pd.DataFrame:
"""
Encode categorical values in 'label' column of train dataset.
Args:
- train (pd.DataFrame): Input DataFrame containing 'label' column
Returns:
- pd.DataFrame: Updated DataFrame with encoded 'label' values
"""
# Create a LabelEncoder instance
label_encoder = LabelEncoder()
# Fit the LabelEncoder to the 'label' column and transform it
# This will replace categorical values with integers
train['label'] = label_encoder.fit_transform(train['label'])
# Return the updated DataFrame
return train
```
**Example Usage:**
```python
train = pd.DataFrame({'label': ['A', 'B', 'A', 'C', 'B']})
train = label_encode_train_data(train)
print(train)
```No explicit import statement is given, but it's assumed that LabelEncoder is from the sklearn.preprocessing module.
The code creates an instance of the LabelEncoder class:
le = LabelEncoder()
The fit method is called on the label encoder instance, passing in the 'label' column of the train dataset:
le.fit(train['label'])
The purpose of this code is to encode the categorical labels in the 'label' column of the train dataset into numerical values. This is a common preprocessing step in machine learning pipelines.