房屋销售价格回归预测的项目有很多人公开了其Kernel, 其中Serigne的“Stacked Regressions to predict House Prices”为多数人所阅读。读者可以在Kaggle网站上直接浏览。本文做了一些总结，把主要的流程步骤列表如下，读者可以厘清思路。
Stacked Regressions to predict House Prices. 0
Log-transformation of the target variable. 7
some numerical variables that are really categorical 9
Label Encoding some categorical variables
that may contain information in their ordering set 9
more important feature. 9
features. 9
dummy categorical features Getting the new train and test sets. 10
Define a cross validation strategy. 10
StackedRegressions to predict House Prices. 0
Log-transformation of the target variable. 7
Transforming some numerical variables that are really categorical 9
categorical variables that may contain information in their ordering set 9
Adding one more important feature. 9
Getting dummy categorical features Getting the new train and test sets. 10
Define a cross validation strategy. 10
Gradient Boosting Regression : 11
Simplest Stacking approach : Averaging base models. 11
Averaged base models class. 11
Averaged base models score. 11
Less simple Stacking : Adding a
Meta-model 12
Stacking averaged Models Class. 13
Stacking Averaged models Score. 13
Ensembling StackedRegressor, XGBoost and LightGBM.. 13
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