---
# System prepended metadata

title: Params Tuning
tags: [kaggle, ' Tabular']

---

---
title: Params Tuning
tags: kaggle, Tabular
description: description.
---

# Params Tuning

[toC]

<!-- Put the link to this slide here so people can follow -->
slide: https://hackmd.io/tReB1wGdRxKtNn13LFffqw

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## Compare

| No | base | tag|  change | local score | lb score | summary |
| -------- | -------- | -------- |-------- | -------- | -------- | -------- |
| 1 |  |baseline |params:<br> nfold:5<br>  objective = 'multiclass',<br>  num_class=6,<br> metric = 'multi_error',<br> max_depth = 8,<br> num_leaves = 127,<br> learning_rate = 0.1,<br> early_stopping_rounds = 200,<br> num_iterations = 5000 <br> | 0.952175  | 0.94375     | |
| 2 |  |baseline2|params:<br> nfold:5<br>  objective = 'multiclass',<br>  num_class=6,<br> metric = 'multi_error',<br> max_depth = 8,<br> num_leaves = 127,<br> learning_rate = 0.05<br> early_stopping_rounds = 1000,<br> num_iterations = 10000 <br> | 0.959971  | 0.94375     | |
| 3 | 1 | RobustScaler | use RobustScaler transform X_Train and X_valid| 0.9582375| 0.94936 | |
| 4 | 1 | StandardScaler | use StandardScaler transform X_Train and X_valid| 0.95521|  | |
| 5 | 1 | MinMaxScaler | use MinMaxScaler transform X_Train and X_valid| 0.95686125|  | RobustScaler is better than standard and minmax,why?|
| 6 | 3 | feature engineer:Elevation | add column <br>`train['EleVerticalHydro'] = train.Elevation - train.Vertical_Distance_To_Hydrology` | 0.95693125 |  | 加了EleVerticalHydro使得elevation的权值减低到6k,同时score降低 |
| 7 | 3 | feature engineer:Elevation | add column <br>`train['Elevation1']=train['Elevation']-train['Elevation'].min()` | 0.9575175 |  | 由于elevation分布在1170-4383之间，所以将所有的值减去1170,但结果是elevation1的importance为0 |
| 8 | 2 | feature engineer:Elevation | add column <br>`train['cut_group']=train['Elevation']//100` | 0.95998 |  | 有了十万分位的提升，不能确定是否改进，先不加入base行列 |
| 9 | 2 | feature engineer:Hydrology | add column<br>`train['Distance_To_Hydrology']=(train.Horizontal_Distance_To_Hydrology**2 + train.Vertical_Distance_To_Hydrology**2)**0.5`  | 0.960035 | 0.95167 | 这个确实有提升啦|
| 10 | 9 | feature engineer:Aspect | add column<br>`train['aspect1']=train['aspect']%360`<br>`train['aspect2']=train['aspect']%180`  | 0.959875 |  | 还降低了 |
| 11 | 2 | add pseudo labels  | download pseudo labels from https://www.kaggle.com/remekkinas/tps12-pseudolabels  | 0.9612625/0.9611775 | 0.95604 |  |
| 12 | 9+11 | feature engineer and pseudo labels | add feature "Distance_To_Hydrology" and pseudo labels | 0.9606875|  |  |
| 13 | 12 | feature engineer  | add feature 'Wilderness_Sum' and 'Soil_Type_Sum' | 0.96098375 |  |  |
| 14 | 1 | 5fold + new baseline |  | 0.9608 | 0.95471 |  |
| 15 | 14 | 5fold +pseudo label + new baseline  |  | 0.9671 | 0.95602 |  |
| 16 |  | pseudo label + new baseline |  | 0.960855 | 0.95503 |  |
| 17 |  | 5fold+pseudo label + old baseline |  | 0.96838352 | 0.95676 | old params better than new params  |