房屋銷(xiāo)售價(jià)格回歸預(yù)測(cè)的項(xiàng)目有很多人公開(kāi)了其Kernel, 其中Serigne的“Stacked Regressions to predict House Prices”為多數(shù)人所閱讀歼疮。讀者可以在Kaggle網(wǎng)站上直接瀏覽外盯。本文做了一些總結(jié)，把主要的流程步驟列表如下扎谎，讀者可以厘清思路路鹰。
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
