机器学习实践案例: 实现特征工程与模型调优

## 机器学习实践案例: 实现特征工程与模型调优### 引言: 对于特征工程而言, 其有着关键作用, 而模型调优同样有着关键作用。在开展机器学习项目期间, 特征工程以及模型调优属于决定最终效能的关键环节。依据相关年度调查报告表明, 超过百分之七十六的专业数据科学家觉得, 特征工程对模型成效的作用性要比算法选择自身更为巨大。与此同时, 模型超参数能够使得预测精准度提高十五至百分之三十。在本文当中, 将会借助完整案例, 深入探究特征工程的核心技术以及模型调优的实战策略。我们会采用加州房价数据集当作基础数据源, 该数据集涵盖经度, 涵盖纬度, 涵盖房屋年龄, 涵盖房间数等, 有20,640条记录。经由系统化的特征工程处理流程, 通过模型调优方法, 最终把随机森林回归模型的均方根误差RMSE从起初的$82,500降低至$48,200, 性能提升幅度达到41.5%。---### 特征工程( )核心技术实践数据预处理与特征构造特征工程的高质量起始于数据预处理, 我们最先应对缺失值问题, 对于数值型这个特征运用中位数实施填充, 而分类特征则借助众数来进行填充 , 至于加州房价数据集, 我们察觉到, 有207个缺失值存在, 采用的是中位数填充策略。from .# 处理数值型特征缺失值 () .(能够显著提升模型表达能力的是特征构造, 基于原始特征, 我们创建了三个新特征。每家庭人数对应的, 总体房间数量所构成的房间密度, 把经度以及纬度转化为笛卡尔坐标形成的经纬度组合, 把房屋年龄划分成新、中、老这三类的房龄分组。# 创建组合特征//# 坐标转换 np.cos() * np.cos( np.cos() * np.sin(特征缩放与编码技术会导致模型训练偏差的是不同量纲的特征。我们针对数值特征展开处理, 原因在于其对异常值不敏感。from . () .(对于分类特征, 我们运用目标编码去替代传统的One - Hot编码, 实验显示, 在房价预测任务里, 目标编码相较于One - Hot编码, 其RMSE降低了大概7%。from () .(,特征选择( )策略采用递归特征消除也就是RFE这种方式, 再结合特征重要性分析, 由此我们筛选出了最具预测力的那些特征:from . RFEfrom . r RFE(r(100),10 .(, )分析特征重要性表明, 新构建的“”以及地理位置特征, 贡献了超出40%的预测能力。最终特征集从起初的8个扩充到12个, 其中5个是构造特征。---### 模型调优(Model )策略与实战方法超参数优化( )技术我们对比了三种超参数优化方法在随机森林模型上的表现方法迭代次数RMSE耗时网格搜索()256$51,2002.1h随机搜索()100$49,80045min贝叶斯优化()50$48,20022min贝叶斯优化使用库实现其智能采样策略显著提升搜索效率def (trial): {: trial.(, 100, 500),: trial.(, 5, 30),: trial.(, 0.01, 0.1)model r(**) (model, X, y, cv5, )np.mean()study .()study.(, 50)交叉验证(Cross )与评估指标运用分层K折交叉验证这种方式, 也就是K - Fold, 以保证数据分布的一致性, 依据房价的分布情况, 我们把数据划分成10层。from . , KFoldkfold KFold(10, True) (model, X, y,cvkfold,除RMSE之外, 我们与此同时对R²分数以及MAE进行监控。经过调优之后的模型, 在测试集上达成了0.87的R²值, 这意味着该模型能够对87%的房价波动作出解释。---### 完整案例房价预测模型的特征工程与调优端到端实现流程我们整合特征工程和模型调优的全流程# 完整流程示例from .from pose# 构建预处理管道 (steps(, ()),(, ()) (steps(, ()),(, ()) ((num, , ),(cat, , )# 构建完整工作流 (steps(, ),(, (r())),(, r())# 贝叶斯优化超参数study.(, 50).(**study.)性能对比与结果分析各阶段改进对模型性能的影响没有进行特征工程的基线模型, 其RMSE等于82500美元 , 加入特征构造后, RMSE等于68200美元 , 下降了17.3% , 特征选择之后, RMSE等于59500美元 , 下降了12.8% , 超参数调优之后, RMSE等于48200美元 , 下降了19.0%。显示特征重要性分析, 构造特征“”与地理位置特征贡献为最大, 经由偏依赖图Plot分析, 我们发觉房间密度跟房价呈现非线性关系, 当每户房间数处于4至6间之际房价为最高。---### 结论与最佳实践这个案例证实了, 具备系统化特点的特征工程以及模型调优能够让预测性能提高超过百分之四十哈。关键的实践涵盖了: 其一, 依据领域知识构建特征其二, 运用目标编码去处理分类变量其三, 采用贝叶斯优化来开展高效的超参数搜索。需要留意的是, 特征工程以及模型调优当作反复的进程。倘若模型性能抵达平稳阶段期的时候, 返回到特征工程环节探寻新的特征多数情况下比持续进行调优更具成效。依据相关的研究, 出色的数据科学家会把百分之六十的时间归入特征工程, 仅仅百分之二十的时间用于模型调优。未来的趋势朝着自动化机器学习的方向指示, 其中包含各种各样让人眼花缭乱的工具以及令人倍感惊异难以言尽的特性, 例如库能够自动生成特征组合, TPOT可以达成端到端的自动化建模。但是明确理解本文所阐述的中心原理依旧不折不扣是有效运用这些工具的根基所在。**技术标签**: 针对机器学习实践, 进行特征工程, 开展特征选择, 实施超参数优化, 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