# Plot The Receiving Operating Characteristic Curve

## Preliminaries

```
/* Load libraries */
from sklearn.datasets import make_classification
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import roc_curve, roc_auc_score
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
```

## Generate Features And Target

```
/* Create feature matrix and target vector */
X, y = make_classification(n_samples=10000,
n_features=10,
n_classes=2,
n_informative=3,
random_state=3)
```

## Split Data Intro Training And Test Sets

```
/* Split into training and test sets */
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=1)
```

## Training Binary Classifier

```
/* Create classifier */
clf = LogisticRegression()
/* Train model */
clf.fit(X_train, y_train)
```

```
LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,
intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,
penalty='l2', random_state=None, solver='liblinear', tol=0.0001,
verbose=0, warm_start=False)
```

## Create Predicted Probabilities

```
/* Get predicted probabilities */
y_score = clf.predict_proba(X_test)[:,1]
```

## Plot Receiving Operating Characteristic Curve

```
/* Create true and false positive rates */
false_positive_rate, true_positive_rate, threshold = roc_curve(y_test, y_score)
/* Plot ROC curve */
plt.title('Receiver Operating Characteristic')
plt.plot(false_positive_rate, true_positive_rate)
plt.plot([0, 1], ls="--")
plt.plot([0, 0], [1, 0] , c=".7"), plt.plot([1, 1] , c=".7")
plt.ylabel('True Positive Rate')
plt.xlabel('False Positive Rate')
plt.show()
```

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