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roc curve

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Innocent Iguana answered on June 29, 2021 Popularity 10/10 Helpfulness 2/10

answer roc curve

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- Diagnostic tool for classification model evaluation.
- It classifies model performance.
- Shows trade-off between sensitivity and specificity (True positive rates vs False positive rates)
- Optimum model is at maximul separation (largest distance between model curve and x=y line)
- Optimum model has the largest area under the ROC curve (also known as ROC AUC)

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Contributed on Feb 09 2023

Innocent Iguana

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# Import necessary modules
from sklearn.metrics import roc_curve

# Compute predicted probabilities: y_pred_prob
y_pred_prob = logreg.predict_proba(X_test)[:,1]

# Generate ROC curve values: fpr, tpr, thresholds
fpr, tpr, thresholds = roc_curve(y_test, y_pred_prob)

# Plot ROC curve
plt.plot([0, 1], [0, 1], 'k--')
plt.plot(fpr, tpr)
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('ROC Curve')
plt.show()

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Source: campus.datacamp.com

Tags: curve curv

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Contributed on Jun 07 2022

josh.ipynb

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roc curve

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# Import roc_curve
from sklearn.metrics import roc_curve

# Generate ROC curve values: fpr, tpr, thresholds
fpr, tpr, thresholds = roc_curve(y_test, y_pred_probs)

plt.plot([0, 1], [0, 1], 'k--')

# Plot tpr against fpr
plt.plot(fpr, tpr)
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('ROC Curve for Diabetes Prediction')
plt.show()

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Source: campus.datacamp.com

Tags: curve curv

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Contributed on Jun 16 2022

josh.ipynb

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roc curve

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y_pred_logreg_proba = classifier_logreg.predict_proba(X_test)
from sklearn.metrics import roc_curve
fpr, tpr, thresholds = roc_curve(y_test, y_pred_logreg_proba[:,1])
plt.figure(figsize=(6,4))
plt.plot(fpr,tpr,'-g',linewidth=1)
plt.plot([0,1], [0,1], 'k--' )
plt.title('ROC curve for Logistic Regression Model')
plt.xlabel("False Positive Rate")
plt.ylabel('True Positive Rate')
plt.show()

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Source: www.analyticsvidhya.com

Tags: curve curv

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Contributed on Jun 29 2021

Xanthous Xenomorph

0 Answers Avg Quality 2/10

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