Correctness of a ROC Curve
I've built a Decision Tree Classifier to practice with the sklearn library. My first task was to shuffle the iris dataset and split it keeping only the last 10 elements for the test. 392
Then, after the training part I predicted the class of these elements and printed other useful metrics to understand what I'm doing. My doubt comes after building the roc curve, since I find it is really different from the other I'v seen in some example. This is the code:
from sklearn.datasets import load_iris
from sklearn.metrics import accuracy_score
from sklearn.metrics import f1_score
from sklearn.metrics import roc_curve, auc
from sklearn.model_selection import train_test_split
from sklearn.metrics import confusion_matrix
from sklearn.model_selection import cross_val_score
import matplotlib.pyplot as plt
from sklearn import tree
import numpy as np
import graphviz
iris = load_iris()
clf_ex1 = tree.DecisionTreeClassifier(criterion="entropy",random_state=300,min_samples_leaf=5,
class_weight={0:1,1:10,2:10})
np.random.seed(0)
indices = np.random.permutation(len(iris.data))
indices_training=indices[:-10]
indices_test=indices[-10:]
iris_X_train = iris.data[indices_training]
iris_y_train = iris.target[indices_training]
iris_X_test = iris.data[indices_test]
iris_y_test = iris.target[indices_test]
clf_ex1 = clf_ex1.fit(iris_X_train, iris_y_train)
predicted_y_test = clf_ex1.predict(iris_X_test)
print("Predictions:")
print(predicted_y_test)
print("True classes:")
print(iris_y_test)
# print some metrics results
acc_score = accuracy_score(iris_y_test, predicted_y_test)
print("--------")
print("Accuracy score: "+ str(acc_score))
print("--------")
f1=f1_score(iris_y_test, predicted_y_test, average='macro')
print("F1 score: "+str(f1))
print("--------")
scores = cross_val_score(clf_ex1, iris.data, iris.target, cv=5)
print("Cross validation scores: "+str(scores))
# Confusion Matrix
print("--------")
print("Confusion matrix:")
print(confusion_matrix(iris_y_test, predicted_y_test))
# Building the ROC Curve
y_test_prob = clf_ex1.predict_proba(iris_X_test)
# Calculating the roc curve for each class changing the pos_label value
fpr_cl0, tpr_cl0, _ = roc_curve(iris_y_test, y_test_prob[:,1], pos_label = 0)
roc_auc_cl0 = auc(fpr_cl0, tpr_cl0)
fpr_cl1, tpr_cl1, _ = roc_curve(iris_y_test, y_test_prob[:,1], pos_label = 1)
roc_auc_cl1 = auc(fpr_cl1, tpr_cl1)
fpr_cl2, tpr_cl2, _ = roc_curve(iris_y_test, y_test_prob[:,1], pos_label = 2)
roc_auc_cl2 = auc(fpr_cl2, tpr_cl2)
# Building the Plot for each class
plt.figure()
lw = 2
plt.plot(fpr_cl0, tpr_cl0, color='darkorange',
lw=lw, label='ROC curve class 0 (area = %0.2f)' % roc_auc_cl0)
plt. 392