<?xml version="1.0" encoding="utf-8" ?><rss version="2.0"><channel><title><![CDATA[Bugabooo的博客]]></title><description><![CDATA[]]></description><link>https://blog.youkuaiyun.com/Bugabooo</link><language>zh-cn</language><generator>https://blog.youkuaiyun.com/</generator><copyright><![CDATA[Copyright &copy; Bugabooo]]></copyright><item><title><![CDATA[python打卡DAY44]]></title><link>https://blog.youkuaiyun.com/Bugabooo/article/details/148562635</link><guid>https://blog.youkuaiyun.com/Bugabooo/article/details/148562635</guid><author>Bugabooo</author><pubDate>Tue, 10 Jun 2025 16:03:26 +0800</pubDate><description><![CDATA[arrange排列函数。# filter数据筛选。# select选择函数。# mutate变形函数。# join数据合并。]]></description><category></category></item><item><title><![CDATA[python打卡DAY43]]></title><link>https://blog.youkuaiyun.com/Bugabooo/article/details/148541843</link><guid>https://blog.youkuaiyun.com/Bugabooo/article/details/148541843</guid><author>Bugabooo</author><pubDate>Mon, 09 Jun 2025 20:06:04 +0800</pubDate><description><![CDATA[arrange排列函数。# filter数据筛选。# select选择函数。# mutate变形函数。# join数据合并。]]></description><category></category></item><item><title><![CDATA[python打卡DAY42]]></title><link>https://blog.youkuaiyun.com/Bugabooo/article/details/148519917</link><guid>https://blog.youkuaiyun.com/Bugabooo/article/details/148519917</guid><author>Bugabooo</author><pubDate>Sun, 08 Jun 2025 22:42:20 +0800</pubDate><description><![CDATA[# arrange排列函数。# filter数据筛选。# select选择函数。# mutate变形函数。# join数据合并。]]></description><category></category></item><item><title><![CDATA[python打卡DAY41]]></title><link>https://blog.youkuaiyun.com/Bugabooo/article/details/148501938</link><guid>https://blog.youkuaiyun.com/Bugabooo/article/details/148501938</guid><author>Bugabooo</author><pubDate>Sat, 07 Jun 2025 22:44:06 +0800</pubDate><description><![CDATA[dim(t2)# View(t2)# dim(t3)# View(t3)# dim(t6)# View(t6)# dim(t7)# str(tt)# str(tt)# View(tt)attach(tt)# View(tt)detach(tt)attach(tt)})str(tt)]]></description><category></category></item><item><title><![CDATA[python打卡DAY40]]></title><link>https://blog.youkuaiyun.com/Bugabooo/article/details/148484553</link><guid>https://blog.youkuaiyun.com/Bugabooo/article/details/148484553</guid><author>Bugabooo</author><pubDate>Sat, 07 Jun 2025 00:31:34 +0800</pubDate><description><![CDATA[=================== 构建 Attention 模型 =====================print(f"最佳测试集损失出现在第{best_epoch}轮，损失值为{best_test_loss:.4f}")# =================== 可视化损失与权重 =====================在第{epoch+1}轮，测试集损失已有{patience}轮未改善。# =================== 数据预处理 =====================]]></description><category></category></item><item><title><![CDATA[python打卡DAY39]]></title><link>https://blog.youkuaiyun.com/Bugabooo/article/details/148409501</link><guid>https://blog.youkuaiyun.com/Bugabooo/article/details/148409501</guid><author>Bugabooo</author><pubDate>Tue, 03 Jun 2025 20:40:49 +0800</pubDate><description><![CDATA[=================== 构建 Attention 模型 =====================print(f"最佳测试集损失出现在第{best_epoch}轮，损失值为{best_test_loss:.4f}")# =================== 可视化损失与权重 =====================在第{epoch+1}轮，测试集损失已有{patience}轮未改善。# =================== 数据预处理 =====================]]></description><category></category></item><item><title><![CDATA[python打卡DAY38]]></title><link>https://blog.youkuaiyun.com/Bugabooo/article/details/148387315</link><guid>https://blog.youkuaiyun.com/Bugabooo/article/details/148387315</guid><author>Bugabooo</author><pubDate>Mon, 02 Jun 2025 21:43:09 +0800</pubDate><description><![CDATA[=================== 构建 Attention 模型 =====================print(f"最佳测试集损失出现在第{best_epoch}轮，损失值为{best_test_loss:.4f}")# =================== 可视化损失与权重 =====================在第{epoch+1}轮，测试集损失已有{patience}轮未改善。# =================== 数据预处理 =====================]]></description><category></category></item><item><title><![CDATA[python打卡DAY37]]></title><link>https://blog.youkuaiyun.com/Bugabooo/article/details/148374273</link><guid>https://blog.youkuaiyun.com/Bugabooo/article/details/148374273</guid><author>Bugabooo</author><pubDate>Mon, 02 Jun 2025 08:53:44 +0800</pubDate><description><![CDATA[=================== 构建 Attention 模型 =====================# # =================== 可视化损失与权重 =====================# =================== 特征列聚类增强 =====================# =================== 数据预处理 =====================print(f"加载第{best_epoch}轮的最佳模型进行最终评估...")]]></description><category></category></item><item><title><![CDATA[python打卡DAY36]]></title><link>https://blog.youkuaiyun.com/Bugabooo/article/details/148352392</link><guid>https://blog.youkuaiyun.com/Bugabooo/article/details/148352392</guid><author>Bugabooo</author><pubDate>Sat, 31 May 2025 15:28:56 +0800</pubDate><description><![CDATA[print(f"最佳测试集损失出现在第{best_epoch}轮，损失值为{best_test_loss:.4f}")# # =================== 可视化损失与权重 =====================# =================== 特征列聚类增强 =====================# =================== 数据预处理 =====================print(f"加载第{best_epoch}轮的最佳模型进行最终评估...")]]></description><category></category></item><item><title><![CDATA[python DAY35]]></title><link>https://blog.youkuaiyun.com/Bugabooo/article/details/148344076</link><guid>https://blog.youkuaiyun.com/Bugabooo/article/details/148344076</guid><author>Bugabooo</author><pubDate>Fri, 30 May 2025 20:03:44 +0800</pubDate><description><![CDATA[=================== 构建 Attention 模型 =====================print(f"最佳测试集损失出现在第{best_epoch}轮，损失值为{best_test_loss:.4f}")# =================== 可视化损失与权重 =====================# =================== 数据预处理 =====================# # 张量具有to(device)方法，可以将张量移动到指定的设备上。]]></description><category></category></item><item><title><![CDATA[python打卡DAY34]]></title><link>https://blog.youkuaiyun.com/Bugabooo/article/details/148314596</link><guid>https://blog.youkuaiyun.com/Bugabooo/article/details/148314596</guid><author>Bugabooo</author><pubDate>Thu, 29 May 2025 17:28:34 +0800</pubDate><description><![CDATA[=================== 构建 Attention 模型 =====================print(f"最佳测试集损失出现在第{best_epoch}轮，损失值为{best_test_loss:.4f}")# # =================== 可视化损失与权重 =====================print(f"早停触发！# =================== 数据预处理 =====================]]></description><category></category></item><item><title><![CDATA[python打卡DAY33]]></title><link>https://blog.youkuaiyun.com/Bugabooo/article/details/148286769</link><guid>https://blog.youkuaiyun.com/Bugabooo/article/details/148286769</guid><author>Bugabooo</author><pubDate>Wed, 28 May 2025 16:31:24 +0800</pubDate><description><![CDATA[=================== 构建 Attention 模型 =====================print(f"最佳测试集损失出现在第{best_epoch}轮，损失值为{best_test_loss:.4f}")# =================== 可视化损失与权重 =====================# =================== 数据预处理 =====================# # 张量具有to(device)方法，可以将张量移动到指定的设备上。]]></description><category></category></item><item><title><![CDATA[python打卡DAY32]]></title><link>https://blog.youkuaiyun.com/Bugabooo/article/details/148266394</link><guid>https://blog.youkuaiyun.com/Bugabooo/article/details/148266394</guid><author>Bugabooo</author><pubDate>Tue, 27 May 2025 22:27:27 +0800</pubDate><description><![CDATA[ordinal_categories=[['< 1 year', '1 year', '2 years', '3 years', '4 years', '5 years', '6 years', '7 years', '8 years', '9 years', '10+ years']] # Years in current job 的顺序 (对应1-11)print(f"最佳测试集损失出现在第{best_epoch}轮，损失值为{best_test_loss:.4f}")print(f"早停触发！]]></description><category></category></item><item><title><![CDATA[python打卡DAY31]]></title><link>https://blog.youkuaiyun.com/Bugabooo/article/details/148242542</link><guid>https://blog.youkuaiyun.com/Bugabooo/article/details/148242542</guid><author>Bugabooo</author><pubDate>Tue, 27 May 2025 00:43:07 +0800</pubDate><description><![CDATA[ordinal_categories=[['< 1 year', '1 year', '2 years', '3 years', '4 years', '5 years', '6 years', '7 years', '8 years', '9 years', '10+ years']] # Years in current job 的顺序 (对应1-11)print(f"最佳测试集损失出现在第{best_epoch}轮，损失值为{best_test_loss:.4f}")print(f"早停触发！]]></description><category></category></item><item><title><![CDATA[python打卡DAY30]]></title><link>https://blog.youkuaiyun.com/Bugabooo/article/details/148209730</link><guid>https://blog.youkuaiyun.com/Bugabooo/article/details/148209730</guid><author>Bugabooo</author><pubDate>Sun, 25 May 2025 16:26:22 +0800</pubDate><description><![CDATA[# y_train和y_test是整数，所以需要转化为long类型，如果是float32，会输出1.0 0.0。# # 将数据转换为 PyTorch 张量，因为 PyTorch 使用张量进行训练。# # # 归一化数据，神经网络对于输入数据的尺寸敏感，归一化是最常见的处理方式。# # 归一化数据，神经网络对于输入数据的尺寸敏感，归一化是最常见的处理方式。# # ##自变量有多个,且量纲不尽相同对其进行归一化减少量纲对结果的影响。# # 张量具有to(device)方法，可以将张量移动到指定的设备上。]]></description><category></category></item><item><title><![CDATA[python打卡DAY29]]></title><link>https://blog.youkuaiyun.com/Bugabooo/article/details/148196726</link><guid>https://blog.youkuaiyun.com/Bugabooo/article/details/148196726</guid><author>Bugabooo</author><pubDate>Sat, 24 May 2025 21:47:57 +0800</pubDate><description><![CDATA[ordinal_categories=[['< 1 year', '1 year', '2 years', '3 years', '4 years', '5 years', '6 years', '7 years', '8 years', '9 years', '10+ years']] # Years in current job 的顺序 (对应1-11)# print(f"训练集 t-SNE耗时: {end_time - start_time:.2f} 秒")]]></description><category></category></item><item><title><![CDATA[python打卡DAY28]]></title><link>https://blog.youkuaiyun.com/Bugabooo/article/details/148178397</link><guid>https://blog.youkuaiyun.com/Bugabooo/article/details/148178397</guid><author>Bugabooo</author><pubDate>Sat, 24 May 2025 00:41:57 +0800</pubDate><description><![CDATA[ordinal_catagories=[['< 1 year', '1 year', '2 years', '3 years', '4 years', '5 years', '6 years', '7 years', '8 years', '9 years', '10+ years']] # Years in current job 的顺序 (对应1-11)# print(f"训练集 t-SNE耗时: {end_time - start_time:.2f} 秒")]]></description><category></category></item><item><title><![CDATA[python 打卡DAY27]]></title><link>https://blog.youkuaiyun.com/Bugabooo/article/details/148151634</link><guid>https://blog.youkuaiyun.com/Bugabooo/article/details/148151634</guid><author>Bugabooo</author><pubDate>Fri, 23 May 2025 00:00:13 +0800</pubDate><description><![CDATA[ordinal_catagories=[['< 1 year', '1 year', '2 years', '3 years', '4 years', '5 years', '6 years', '7 years', '8 years', '9 years', '10+ years']] # Years in current job 的顺序 (对应1-11)# # from sklearn.linear_model import LogisticRegression #逻辑回归分类器。]]></description><category></category></item><item><title><![CDATA[python打卡DAY26]]></title><link>https://blog.youkuaiyun.com/Bugabooo/article/details/148125772</link><guid>https://blog.youkuaiyun.com/Bugabooo/article/details/148125772</guid><author>Bugabooo</author><pubDate>Wed, 21 May 2025 22:40:06 +0800</pubDate><description><![CDATA[ordinal_catagories=[['< 1 year', '1 year', '2 years', '3 years', '4 years', '5 years', '6 years', '7 years', '8 years', '9 years', '10+ years']] # Years in current job 的顺序 (对应1-11)# kmeans_label=kmeans.fit_predict(x_scaled)#提供了每个数据点所属的簇的信息，用于区分不同簇的数据点。]]></description><category></category></item><item><title><![CDATA[python打卡DAY25]]></title><link>https://blog.youkuaiyun.com/Bugabooo/article/details/148101070</link><guid>https://blog.youkuaiyun.com/Bugabooo/article/details/148101070</guid><author>Bugabooo</author><pubDate>Tue, 20 May 2025 23:51:36 +0800</pubDate><description><![CDATA[ordinal_categories = [['< 1 year', '1 year', '2 years', '3 years', '4 years', '5 years', '6 years', '7 years', '8 years', '9 years', '10+ years']] # Years in current job 的顺序 (对应1-11)# # from sklearn.linear_model import LogisticRegression #逻辑回归分类器。]]></description><category></category></item></channel></rss>