Sklearn ROC AUC Score : ValueError: y should be a 1d array, got an array of shape (15, 2) instead

Solution 1:

Your output from model.predict_proba() is a matrix with 2 columns, one for each class. To calculate roc, you need to provide the probability of the positive class:

Using an example dataset:

from sklearn.datasets import make_classification
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import roc_auc_score
from sklearn.model_selection import train_test_split

X, y = make_classification(n_classes=2)
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.33, random_state=42)
rf = RandomForestClassifier()
model = rf.fit(X_train, y_train)
y_proba = model.predict_proba(X_test)

It looks like this:

array([[0.69, 0.31],
       [0.13, 0.87],
       [0.94, 0.06],
       [0.94, 0.06],
       [0.07, 0.93]])

Then do:

roc_auc_score(y_test, y_proba[:,1])