## Intro

Some times you will be tasked to fit a model to data with high variance, as in the data varies from the average a lot. In such cases, we can use regularization techniques. Logisitc regression over some options for regularization. In this article, we will see how to use regularization with Logistic Regression in Sklearn.

## Regularizing Logistic Regression

To regularize a logistic regression model, we can use two paramters `penalty`

and `Cs`

(cost). In practice, we would use something like `GridCV`

or a loop to try multipel paramters and pick the best model from the group. Below is an example of how to specify these parameters on a logisitc regression model.

```
from sklearn.linear_model import LogisticRegressionCV
from sklearn import datasets
from sklearn.preprocessing import StandardScaler
iris = datasets.load_iris()
features = iris.data
target = iris.target
scaler = StandardScaler()
features_standardized = scaler.fit_transform(features)
logistic_regression = LogisticRegressionCV(
penalty='l2',
Cs=10
)
model = logistic_regression.fit(features_standardized, target)
print(mode.score())
```