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Update app.py
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app.py
CHANGED
@@ -91,8 +91,7 @@ if uploaded_file is not None:
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st.pyplot(fig)
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# ROC 曲線
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#
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# 將 y_test 標籤轉換為 0 和 1
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y_test_binary = (y_test == 2).astype(int) # 假設 2 是正標籤
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# 計算 ROC 曲線
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@@ -102,7 +101,6 @@ if uploaded_file is not None:
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fpr_vote, tpr_vote, _ = roc_curve(y_test_binary, y_pred_vote_proba)
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roc_auc_vote = auc(fpr_vote, tpr_vote)
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fig, ax = plt.subplots()
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ax.plot(fpr_stack, tpr_stack, color='blue', lw=2, label='堆疊分類器 (AUC = %0.2f)' % roc_auc_stack)
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ax.plot(fpr_vote, tpr_vote, color='red', lw=2, label='投票分類器 (AUC = %0.2f)' % roc_auc_vote)
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@@ -113,4 +111,4 @@ if uploaded_file is not None:
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ax.set_ylabel('真陽性率(True Positive Rate)')
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ax.set_title('ROC 曲線')
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ax.legend(loc="lower right")
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st.pyplot(fig)
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st.pyplot(fig)
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# ROC 曲線
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# 將 y_test 標籤轉換為 0 和 1
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y_test_binary = (y_test == 2).astype(int) # 假設 2 是正標籤
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# 計算 ROC 曲線
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fpr_vote, tpr_vote, _ = roc_curve(y_test_binary, y_pred_vote_proba)
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roc_auc_vote = auc(fpr_vote, tpr_vote)
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fig, ax = plt.subplots()
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ax.plot(fpr_stack, tpr_stack, color='blue', lw=2, label='堆疊分類器 (AUC = %0.2f)' % roc_auc_stack)
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ax.plot(fpr_vote, tpr_vote, color='red', lw=2, label='投票分類器 (AUC = %0.2f)' % roc_auc_vote)
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ax.set_ylabel('真陽性率(True Positive Rate)')
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ax.set_title('ROC 曲線')
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ax.legend(loc="lower right")
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st.pyplot(fig)
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