scaler.fit_transform()

The fit_transform() method is a convenient way to apply both the fit() and transform() methods on a data set.

Scaler.fit_transform() is a method in the scikit-learn library, which is used to standardize the data by subtracting the mean and dividing by the standard deviation.

The fit() method is used to compute the mean and standard deviation of the training data, which is then used to transform the data using the transform() method.

The scaler.fit_transform() method is used to fit the scaler on the training data and then transform it to standardized data in a single step.

Here is an example of using the scaler.fit_transform() method:

from sklearn.preprocessing import StandardScaler
import numpy as np

# create some sample data
data = np.array([[1, 2], [3, 4], [5, 6]])

# create a scaler object
scaler = StandardScaler()

# fit and transform the data
normalized_data = scaler.fit_transform(data)

print(normalized_data)

Output:

[[-1.22474487 -1.22474487]
 [ 0.          0.        ]
 [ 1.22474487  1.22474487]]

In the above example, we create a 2-dimensional array of data and then create a StandardScaler object. We then use the scaler.fit_transform() method to fit the scaler on the data and transform it to standardized data. The output shows the standardized data.