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plotting roc curve

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josh.ipynb answered on July 11, 2020 Popularity 10/10 Helpfulness 3/10

answer plotting roc curve

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plotting roc curve

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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()

Popularity 10/10 Helpfulness 3/10 Language python

Source: campus.datacamp.com

Tags: curve python roc

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Contributed on Jun 07 2022

josh.ipynb

0 Answers Avg Quality 2/10

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roc curve python

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import sklearn.metrics as metrics
# calculate the fpr and tpr for all thresholds of the classification
probs = model.predict_proba(X_test)
preds = probs[:,1]
fpr, tpr, threshold = metrics.roc_curve(y_test, preds)
roc_auc = metrics.auc(fpr, tpr)

# method I: plt
import matplotlib.pyplot as plt
plt.title('Receiver Operating Characteristic')
plt.plot(fpr, tpr, 'b', label = 'AUC = %0.2f' % roc_auc)
plt.legend(loc = 'lower right')
plt.plot([0, 1], [0, 1],'r--')
plt.xlim([0, 1])
plt.ylim([0, 1])
plt.ylabel('True Positive Rate')
plt.xlabel('False Positive Rate')
plt.show()

# method II: ggplot
from ggplot import *
df = pd.DataFrame(dict(fpr = fpr, tpr = tpr))
ggplot(df, aes(x = 'fpr', y = 'tpr')) + geom_line() + geom_abline(linetype = 'dashed')

Popularity 10/10 Helpfulness 10/10 Language python

Source: stackoverflow.com

Tags: curve curv

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Contributed on Jul 11 2020

Better Beaver

0 Answers Avg Quality 2/10

roc auc score plotting

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import scikitplot as skplt
import matplotlib.pyplot as plt

y_true = # ground truth labels
y_probas = # predicted probabilities generated by sklearn classifier
skplt.metrics.plot_roc_curve(y_true, y_probas)
plt.show()

Popularity 10/10 Helpfulness 4/10 Language python

Source: stackoverflow.com

Tags: auc

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Contributed on May 26 2021

Successful Sloth

0 Answers Avg Quality 2/10

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)

Popularity 10/10 Helpfulness 2/10 Language whatever

Source: Grepper

Tags: curve curv

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Contributed on Feb 09 2023

Innocent Iguana

0 Answers Avg Quality 2/10

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()

Popularity 10/10 Helpfulness 2/10 Language python

Source: campus.datacamp.com

Tags: curve curv

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Contributed on Jun 16 2022

josh.ipynb

0 Answers Avg Quality 2/10

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()

Popularity 10/10 Helpfulness 2/10 Language python

Source: www.analyticsvidhya.com

Tags: curve curv

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Contributed on Jun 29 2021

Xanthous Xenomorph

0 Answers Avg Quality 2/10

plotting roc curve

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