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%matplotlib inline
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import matplotlib.pyplot as plt
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.metrics import plot_roc_curve
# Models to compare
from sklearn.svm import SVC
from sklearn.ensemble import RandomForestClassifier
from sklearn.naive_bayes import MultinomialNB
from sklearn.neural_network import MLPClassifier
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data = pd.read_csv('pima-indians-diabetes.data.csv', header=None)
X, y = data.iloc[:,:-1], data.iloc[:,-1]
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)
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ax = None
for model in [SVC(),
RandomForestClassifier(),
MultinomialNB(),
MLPClassifier(max_iter=300)]:
model.fit(X_train, y_train)
if ax is None:
plot_roc_curve(model, X_test, y_test)
ax = plt.gca()
else:
plot_roc_curve(model, X_test, y_test, ax=ax)
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