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@oguzhari
Last active June 24, 2022 10:41
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ROC-AUC Oranları
#Algoritma Bazlı ROC-AUC Skoru Hesaplama.
rf_predictions = clf.predict(X_test)
rf_probs = clf.predict_proba(X_test)[:, 1]
score= roc_auc_score(y_test, rf_probs)
rf_predictions = lr.predict(X_test)
rf_probs = lr.predict_proba(X_test)[:, 1]
score2= roc_auc_score(y_test, rf_probs)
rf_predictions = dtc.predict(X_test)
rf_probs = dtc.predict_proba(X_test)[:, 1]
score3= roc_auc_score(y_test, rf_probs)
rf_predictions = rfc.predict(X_test)
rf_probs = rfc.predict_proba(X_test)[:, 1]
score4= roc_auc_score(y_test, rf_probs)
rf_predictions = gradient.predict(X_test)
rf_probs = gradient.predict_proba(X_test)[:, 1]
score5= roc_auc_score(y_test, rf_probs)
rf_predictions = xgb.predict(X_test)
rf_probs = xgb.predict_proba(X_test)[:, 1]
score6= roc_auc_score(y_test, rf_probs)
methods= ["Naive Bayes","Logistik Regresyon", "Karar Ağacı","Rassal Orman", "Gradient Arttırma","XGBoost"]
roc_auc = [score.mean(),score2.mean(),score3.mean(),score4.mean(),score5.mean(),score6.mean()]
sns.set()
plt=reload(plt)
plt.figure(figsize = (16,9))
plt.title('Algoritmalara Göre ROC-AUC Oranı', fontsize = 20)
plt.ylabel="Uygulanan Algoritmalar"
plt.xlabel="Başarı Oranı"
plt.xticks(fontsize=14)
plt.yticks(fontsize=14)
sns.barplot(x=roc_auc, y= methods)
for line in range(len(methods)):
plt.text(0.65,line-0.15,"{:.2f}%".format(roc_auc[line]*100),horizontalalignment ='left',size ='large', color='black')
plt.savefig('fig/ROC-AUC',dpi=600)
plt.show()
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