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plot multiple ROC in python

#set up plotting area
plt.figure(0).clf()

#fit logistic regression model and plot ROC curve
model = LogisticRegression()
model.fit(X_train, y_train)
y_pred = model.predict_proba(X_test)[:, 1]
fpr, tpr, _ = metrics.roc_curve(y_test, y_pred)
auc = round(metrics.roc_auc_score(y_test, y_pred), 4)
plt.plot(fpr,tpr,label="Logistic Regression, AUC="+str(auc))

#fit gradient boosted model and plot ROC curve
model = GradientBoostingClassifier()
model.fit(X_train, y_train)
y_pred = model.predict_proba(X_test)[:, 1]
fpr, tpr, _ = metrics.roc_curve(y_test, y_pred)
auc = round(metrics.roc_auc_score(y_test, y_pred), 4)
plt.plot(fpr,tpr,label="Gradient Boosting, AUC="+str(auc))

#add legend
plt.legend()
Comment

plot multiple ROC in python

#set up plotting area
plt.figure(0).clf()

#fit logistic regression model and plot ROC curve
model = LogisticRegression()
model.fit(X_train, y_train)
y_pred = model.predict_proba(X_test)[:, 1]
fpr, tpr, _ = metrics.roc_curve(y_test, y_pred)
auc = round(metrics.roc_auc_score(y_test, y_pred), 4)
plt.plot(fpr,tpr,label="Logistic Regression, AUC="+str(auc))

#fit gradient boosted model and plot ROC curve
model = GradientBoostingClassifier()
model.fit(X_train, y_train)
y_pred = model.predict_proba(X_test)[:, 1]
fpr, tpr, _ = metrics.roc_curve(y_test, y_pred)
auc = round(metrics.roc_auc_score(y_test, y_pred), 4)
plt.plot(fpr,tpr,label="Gradient Boosting, AUC="+str(auc))

#add legend
plt.legend()
Comment

plot multiple ROC in python

#set up plotting area
plt.figure(0).clf()

#fit logistic regression model and plot ROC curve
model = LogisticRegression()
model.fit(X_train, y_train)
y_pred = model.predict_proba(X_test)[:, 1]
fpr, tpr, _ = metrics.roc_curve(y_test, y_pred)
auc = round(metrics.roc_auc_score(y_test, y_pred), 4)
plt.plot(fpr,tpr,label="Logistic Regression, AUC="+str(auc))

#fit gradient boosted model and plot ROC curve
model = GradientBoostingClassifier()
model.fit(X_train, y_train)
y_pred = model.predict_proba(X_test)[:, 1]
fpr, tpr, _ = metrics.roc_curve(y_test, y_pred)
auc = round(metrics.roc_auc_score(y_test, y_pred), 4)
plt.plot(fpr,tpr,label="Gradient Boosting, AUC="+str(auc))

#add legend
plt.legend()
Comment

plot multiple ROC in python

#set up plotting area
plt.figure(0).clf()

#fit logistic regression model and plot ROC curve
model = LogisticRegression()
model.fit(X_train, y_train)
y_pred = model.predict_proba(X_test)[:, 1]
fpr, tpr, _ = metrics.roc_curve(y_test, y_pred)
auc = round(metrics.roc_auc_score(y_test, y_pred), 4)
plt.plot(fpr,tpr,label="Logistic Regression, AUC="+str(auc))

#fit gradient boosted model and plot ROC curve
model = GradientBoostingClassifier()
model.fit(X_train, y_train)
y_pred = model.predict_proba(X_test)[:, 1]
fpr, tpr, _ = metrics.roc_curve(y_test, y_pred)
auc = round(metrics.roc_auc_score(y_test, y_pred), 4)
plt.plot(fpr,tpr,label="Gradient Boosting, AUC="+str(auc))

#add legend
plt.legend()
Comment

Plot Multiple ROC Curves in Python

#set up plotting area
plt.figure(0).clf()

#fit logistic regression model and plot ROC curve
model = LogisticRegression()
model.fit(X_train, y_train)
y_pred = model.predict_proba(X_test)[:, 1]
fpr, tpr, _ = metrics.roc_curve(y_test, y_pred)
auc = round(metrics.roc_auc_score(y_test, y_pred), 4)
plt.plot(fpr,tpr,label="Logistic Regression, AUC="+str(auc))

#fit gradient boosted model and plot ROC curve
model = GradientBoostingClassifier()
model.fit(X_train, y_train)
y_pred = model.predict_proba(X_test)[:, 1]
fpr, tpr, _ = metrics.roc_curve(y_test, y_pred)
auc = round(metrics.roc_auc_score(y_test, y_pred), 4)
plt.plot(fpr,tpr,label="Gradient Boosting, AUC="+str(auc))

#add legend
plt.legend()
Comment

plot multiple ROC in python

#set up plotting area
plt.figure(0).clf()

#fit logistic regression model and plot ROC curve
model = LogisticRegression()
model.fit(X_train, y_train)
y_pred = model.predict_proba(X_test)[:, 1]
fpr, tpr, _ = metrics.roc_curve(y_test, y_pred)
auc = round(metrics.roc_auc_score(y_test, y_pred), 4)
plt.plot(fpr,tpr,label="Logistic Regression, AUC="+str(auc))

#fit gradient boosted model and plot ROC curve
model = GradientBoostingClassifier()
model.fit(X_train, y_train)
y_pred = model.predict_proba(X_test)[:, 1]
fpr, tpr, _ = metrics.roc_curve(y_test, y_pred)
auc = round(metrics.roc_auc_score(y_test, y_pred), 4)
plt.plot(fpr,tpr,label="Gradient Boosting, AUC="+str(auc))

#add legend
plt.legend()
Comment
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