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pythonscikit-learnrocauc

Miss-matching ROC and AUC calculations in python

I am currently using python to train a random forest model. I initially tried to compute the ROC curve representations as follows:

import scikitplot as skplt
from sklearn.metrics import RocCurveDisplay
import sklearn
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn import metrics

model2_bert = RandomForestClassifier(bootstrap=False, max_depth=None, max_features='auto', min_samples_leaf=5, min_samples_split=5, n_estimators=50)
rf1 = model2_bert.fit(X_train, y_train)    
y_hat = rf1.predict(X_test)
ax = plt.gca()
rfc_disp = RocCurveDisplay.from_estimator(rf1, X_test, y_test, ax=ax)
plt.show()

This gives me the figure :

enter image description here

As I was getting some AUC=1 for some specifications, I decided to try to calculate this in other ways.

fpr, tpr, thresholds = metrics.roc_curve(y_test, y_hat)
roc_auc = metrics.auc(fpr, tpr)
roc_auc
# method I: 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()

enter image description here

And the third method :

y_prob = rf.predict_proba(X_test)
rf1 = model2_bert.fit(X_train, y_train)

    skplt.metrics.plot_roc_curve(y_test, y_prob)
    metrics.plot_roc_curve(rf1, X_test, y_test)
    plt.show()

enter image description here

It seems like the last two coincide and are based on the calculated AUC. What is wrong with the first graph, then?

PS: This is the confusion matris so AUC=0.97 seems to high for me. I even got AUC=1 in the first figure for some specifications...

enter image description here

Solution

  • I think the problem is that y_hat = rf1.predict(X_test) is returning binary classification output (0 and 1). For the ROC AUC you need a probability or score. Instead, you should use predict_proba:

    y_prob = rf1.predict_proba(X_test)
fpr, tpr, thresholds = metrics.roc_curve(y_test, y_prob )
.... 
<rest of your code>