## Intro

Logisitic Regression, despite its name, is used as a classifier (puts items into cateogires). It uses a similar algorithm to linear regression, hence the name. The basic logistic regression is used as a binary classifier and can categorize items into two groups. In this article, we will learn how to build a Binary Classifier with Logisitic Regression in Sklearn.

## Creating a Binary Classifier

To create a Binary Classifier, we use the `LogisticRegression`

class from the `linear_model`

package. We create an instance of this class, then pass our features (x values) and target (y value) to the `fit`

method. Below, we use a subset of the iris dataset to classify into two groups.

```
from sklearn.linear_model import LogisticRegression
from sklearn import datasets
from sklearn.preprocessing import StandardScaler
iris = datasets.load_iris()
features = iris.data[:100,:]
target = iris.target[:100]
logistic_regression = LogisticRegression()
model = logistic_regression.fit(features, target)
print(model.score())
```