* <strong>過擬合</strong>：就是在訓練集上準確率非常高，而在測試集上準確率低。 * <strong>分類問題評價方法</strong>：[https://www.zhihu.com/question/30643044](https://www.zhihu.com/question/30643044) * <strong>分類模型評估之ROC-AUC曲線和RPC曲線</strong>：[https://blog.csdn.net/pipisorry/article/details/51788927](https://blog.csdn.net/pipisorry/article/details/51788927) * <strong>混淆矩陣記憶口訣</strong>：包含主對角線（真正、真反），剩余（假正，假反）。  * [模型評估方法（混淆矩陣、查準率&查全率&P-R圖）](https://blog.csdn.net/zzh1301051836/article/details/88965040) * [欠擬合的解決方案有哪些？](https://support.huaweicloud.com/modelarts_faq/modelarts_05_0170.html) * scikit-learn算法選擇路徑：  * [推薦系統中SVD算法詳解](https://blog.csdn.net/fool_ran/article/details/79384040) * [scikit-learn算法選擇路徑解釋](https://blog.csdn.net/hjwbit/article/details/88065566?utm_medium=distribute.pc_relevant.none-task-blog-BlogCommendFromBaidu-1.control&depth_1-utm_source=distribute.pc_relevant.none-task-blog-BlogCommendFromBaidu-1.control) * [python 機器學習：sklearn全景圖](https://blog.csdn.net/huoyingchong64/article/details/89879134?utm_medium=distribute.pc_relevant_t0.none-task-blog-BlogCommendFromMachineLearnPai2-1.control&depth_1-utm_source=distribute.pc_relevant_t0.none-task-blog-BlogCommendFromMachineLearnPai2-1.control) * [scikit-learn文檔](https://sklearn.apachecn.org/docs/master/11.html)