In LR Classifier, he probabilities describing the possible outcomes of a single trial are modeled using a logistic function. It is implemented in the `linear_model`

library

```
from sklearn.linear_model import LogisticRegression
```

The sklearn LR implementation can fit binary, One-vs- Rest, or multinomial logistic regression with optional L2 or L1 regularization. For example, let us consider a binary classification on a sample sklearn dataset

```
from sklearn.datasets import make_hastie_10_2
X,y = make_hastie_10_2(n_samples=1000)
```

Where X is a `n_samples X 10`

array and y is the target labels -1 or +1.

Use train-test split to divide the input data into training and test sets (70%-30%)

```
from sklearn.model_selection import train_test_split
#sklearn.cross_validation in older scikit versions
data_train, data_test, labels_train, labels_test = train_test_split(X,y, test_size=0.3)
```

Using the LR Classifier is similar to other examples

```
# Initialize Classifier.
LRC = LogisticRegression()
LRC.fit(data_train, labels_train)
# Test classifier with the test data
predicted = LRC.predict(data_test)
```

Use Confusion matrix to visualise results

```
from sklearn.metrics import confusion_matrix
confusion_matrix(predicted, labels_test)
```

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