scaler.fit_transform
时间: 2023-09-24 08:14:07 浏览: 51
The `fit_transform()` method in Scikit-learn's `Scaler` class is a convenience method that applies both the `fit()` and `transform()` methods to the data.
The `fit()` method estimates the parameters (e.g., mean and standard deviation) needed for scaling the data, while the `transform()` method applies the scaling using the estimated parameters.
The `fit_transform()` method combines these two steps, allowing us to fit and transform the data in one step.
Here is an example of how to use `fit_transform()` to standardize the data:
```
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
X = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
X_scaled = scaler.fit_transform(X)
```
Here, `X` is a 3x3 matrix representing the original data. We create a `StandardScaler` object and apply the `fit_transform()` method to `X`, which returns a new matrix `X_scaled` that has been standardized.