labelencoder.fit_transform
时间: 2023-09-29 22:03:07 浏览: 62
The method `fit_transform()` of the `LabelEncoder` class in scikit-learn is used to both fit and transform the input data into a numerical format suitable for machine learning models.
`fit_transform()` takes a single input argument, which is the data that needs to be encoded. It first fits the encoder to the input data by identifying the unique labels in the data and assigning a numerical value to each label. Then, it transforms the input data by replacing each label with its corresponding numerical value.
Here's an example of how to use `fit_transform()`:
```python
from sklearn.preprocessing import LabelEncoder
# sample data
data = ['red', 'blue', 'green', 'blue', 'red', 'green']
# create an instance of LabelEncoder
encoder = LabelEncoder()
# fit and transform the data
encoded_data = encoder.fit_transform(data)
print(encoded_data)
# Output: [2 0 1 0 2 1]
```
In this example, the `LabelEncoder` object is used to encode the list of colors. `fit_transform()` is called on the `encoder` object with `data` as the input argument. The output is an array of integers, where each color has been replaced by its corresponding numerical value.
Note that the numerical values assigned to each label are arbitrary and do not reflect any inherent order or ranking in the data.
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