https://github.com/hycis/unilever_pred

This repo for the unilever product score prediction competition
https://github.com/hycis/unilever_pred

Last synced: 4 days ago
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This repo for the unilever product score prediction competition

• Host: GitHub
• URL: https://github.com/hycis/unilever_pred
• Owner: hycis
• Created: 2014-12-29T06:41:24.000Z (about 10 years ago)
• Default Branch: master
• Last Pushed: 2015-04-23T10:15:54.000Z (over 9 years ago)
• Last Synced: 2024-12-27T14:39:27.792Z (20 days ago)
• Language: Python
• Size: 609 KB
• Stars: 0
• Watchers: 3
• Forks: 0
• Open Issues: 1
• Metadata Files:
  • Readme: README.md

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README

        
# unilever_pred

This repo for the unilever product score prediction competition

__Things To Do__

1. Find the best number of top features using cross-validation

2. Find the best number of estimators and learning rate for GradientBoostingRegressor using cross-validation

# TODO: how to interpolate test data?

# TODO: check out the feature weights

# TODO: recursive feature elimination

# TODO: do the avg plots and auto-detect gradient

# TODO: interpolatio by label

# TODO: which ingredient correlate with which attribute

# TODO: correlation with OO score

Test Labels

===========

135,6

2212,6

test size=2525

=======

Model ID | Model | Cross-Validation Score | Test Score | Remarks | Features

---------|------ | ---------------------- | ---------- | --------| --------

        | GradientBoostingRegressor  | | 0.222446 | Learning-rate = 0.1, n_estimators = 100 | [158:]

        | GradientBoostingRegressor  | | 0.221658 | Learning-rate = 0.1, n_estimators = 100 | [1:]

        | GradientBoostingRegressor  | | 0.218353 | Learning-rate = 0.1, n_estimators = 100-140, max-depth=5 or 10 | top 101  features

        | GradientBoostingRegressor  | | 0.217173 | Learning-rate = 0.08, n_estimators = 100-140, max-depth=5,7,9 | na_Zero,no_ingre_prob

        | GradientBoostingRegressor  | | 0.21678 | Learning-rate = 0.08, n_estimators = 140, max-depth=7 | na_Zero,no_ingre_prob

        | GradientBoostingRegressor  | | 0.21366 | learning_rate=0.07, n_estimators=200, max-depth=6 | na_zero,no_ingre_prob

        | GradientBoostingRegressor  | | 0.212674 | learning_rate=0.07, n_estimators=280, max-depth=6 | na_zero,no_ingre_prob

        | GradientBoostingRegressor  | | 0.212533 | learning_rate=0.07, n_estimators=380, max-depth=6 | na_zero,no_ingre_prob

        | GradientBoostingRegressor |          | 0.222446 | Learning-rate = 0.1, n_estimators = 100 | [158:]

        | GradientBoostingRegressor |          | 0.221658 | Learning-rate = 0.1, n_estimators = 100 | [1:]

        | AverageModel              | 0.195236 | 0.222848 | Average of rfr, etr, gbr, br, br                     | [158:]

        | AverageModel              | 0.196189 |          | Average of rfr, etr, gbr, br, br(br)                 | Top 50

        | AverageModel              | 0.192408 | 0.221837 | Average of rfr, etr, gbr, br, br, svr                | Top 50

        | AverageModel              | 0.192768 |  | Average of rfr, gbr, br, br, svr                             | Top 50

        | AverageModel              | 0.191267 | 0.218865 | Average of  rfr, etr, svr, gbr, br, br(gbr), br(gbr) | Top 50

ave_model1 | AverageMode            | 0.1896 | 0.21511  |                                                        | Top 100

Phase 2: MSE

Rank

Model| CV score  | Pub Score | Params

-----|-----------|-----------|--------

RF   |           | .483824   | max_depth=4_max_features=15_n_estimators=350

RF   |           | .4103     | max_depth=5_max_features=15_n_estimators=250

RF   |           | .398529   | max_depth=5_max_features=12_n_estimators=210

RF   |           | .397794   | max_depth=5_max_features=12_n_estimators=210 and max_depth=5_max_features=15_n_estimators=250

Model| CV score  | Pub Score | Params

-----|-----------|-----------|--------

GBR  |           | 0.5156    | learning_rate=0.05_max_depth=2_n_estimators=200

GBR  |           | 0.5153    | learning_rate=0.05_max_depth=2_n_estimators=150

GBR  |           | 0.5152    | learning_rate=0.04_max_depth=2_n_estimators=140