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AUC ROC Skillful model comparison
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import matplotlib.pyplot as plt | |
# ROC and AUC modules | |
from sklearn.datasets import make_classification | |
from sklearn.linear_model import LogisticRegression | |
from sklearn.model_selection import train_test_split | |
from sklearn.metrics import roc_curve, roc_auc_score | |
import seaborn as sns | |
# generate 2 class dataset | |
X, y = make_classification(n_samples=1000, n_classes=2, weights=[0.5], random_state=1) | |
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.5, random_state=2) | |
no_skill_prob = [0 for val in range(len(y_test))] | |
model = LogisticRegression(solver='lbfgs') | |
model.fit(X_train, y_train) | |
logis_prob = model.predict_proba(X_test) | |
logis_prob = logis_prob[:, 1] | |
no_skill_auc = roc_auc_score(y_test, no_skill_prob) | |
logis_reg_auc = roc_auc_score(y_test, logis_prob) | |
'No model skill: ROC AUC: {}'.format(no_skill_auc) | |
# 'No model skill: ROC AUC: 0.5' | |
'Logistic model skill: ROC AUC: {}'.format(round(logis_reg_auc, 3)) | |
#'Logistic model skill: ROC AUC: 0.903' | |
no_skill_fpr, no_skill_tpr, _ = roc_curve(y_test, no_skill_prob) | |
log_res_fpr, log_res_tpr, _ = roc_curve(y_test, logis_prob) | |
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