StandardScaler is a preprocessing technique used in machine learning to standardize the features by removing the mean and scaling them to unit variance. This method is widely used in many machine learning algorithms, especially those that are sensitive to the scale of input features.
The formula for standardizing a feature �x is:
�=�−��z=σx−μ​
where �μ is the mean of the feature, and �σ is the standard deviation.
Using StandardScaler can make your algorithms perform better since many algorithms assume that all features are centered around zero and have a similar variance.
Here’s an example of how you can use StandardScaler in Python’s Scikit-Learn library:
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from sklearn.preprocessing import StandardScaler
from sklearn.datasets import load_iris

# Load dataset
iris = load_iris()
X = iris.data

# Create StandardScaler object
scaler = StandardScaler()

# Fit and transform the data
X_scaled = scaler.fit_transform(X)
By transforming the features using StandardScaler, you are ensuring that they are appropriately scaled, which often leads to better performance of machine learning models.