/unilever_pred

This repo for the unilever product score prediction competition

Primary LanguagePython

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