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2019-kaggle-elo-top-11-solution

https://www.kaggle.com/c/elo-merchant-category-recommendation

This is the 11th place solution writeup. Hello, I would like to congratulate everyone for the efforts they put into this competition. And ELO for hosting such a great competition with interesting data and hair pulling outliers :D

I would like to give special thanks to my teammates @Zakaria EL Mesaoudi @Nabil @Lindada for their skills, perseverance and hardwork.

The huge gap between CV and LB did not make it easy for us (and for everyone I imagine). Those that persevered until the end would perfectly understand.

Congrats for all the medalists, you did a tremendous job :D

SUMMARY

So basically we were two separate teams that merged a couple of hours before the deadline. Each had had completed a tremendous amount of work, and after almost running out of new ideas, a merge was deemed necessary (and fruitful).

FEATURE ENGINEERING

Like most public kernels, we constructed aggregation features. Here's a list of the our strongest ones:

  1. I refer the Kaggle Rank System Compute Formula（link:[https://www.kaggle.com/progression][4]) df_data['duration_sqrt_counts'] = df_data['durations']/sqrt(df_data['card_id_counts']) df_data['duration_log1p_counts'] = df_data['durations']/log1p(df_data['card_id_counts']) df_data['duration_counts'] = df_data['durations']/df_data['card_id_counts']

  2. Categorical features: frequence, Maxfrequence, MaxfrequenceRatio and FM

  3. card_id/merchant_id/mechant_category_id/city_id (visit sequence to sequence embedding by using Word2vec)

  4. purchase_amount:hist/new and using NMF to get the features

  5. features interactions between hist/new df['purchase_amount_ratio_v3'] = df['new_purchase_amount_max']/df['hist_purchase_amount_sum'] df['purchase_amount_diff_v1'] = df['new_purchase_amount_sum']-df['hist_purchase_amount_sum'] df['purchase_amount_diff_v2'] = df['new_purchase_amount_mean']-df['hist_purchase_amount_mean'] df['purchase_amount_diff_v3'] = df['new_purchase_amount_max']-df['hist_purchase_amount_max'] df['purchase_amount_diff_v4'] = df['new_purchase_amount_min']-df['hist_purchase_amount_min'] df['pa_mlag_ratio'] = df['new_purchase_amount_sum']/(df['month_lag_mean'] - 1) df['pa_new_hist_ratio'] = df['new_purchase_amount_sum']/(df['hist_purchase_amount_sum']) df['pa_new_hist_mean_ratio'] = df['new_purchase_amount_mean']/(df['hist_purchase_amount_mean'] ) df['pa_new_hist_min_ratio'] = df['new_purchase_amount_min']/(df['hist_purchase_amount_min'] ) df['pa_new_hist_max_ratio'] = df['new_purchase_amount_max']/(df['hist_purchase_amount_max'] ) We had two separate feature sets. One with +1000 features and another one with +200 features

At this point our best models scored around: CV 3.642X and LB: 3.688 and CV 3.644X and LB: 3.686

After that, we took the correlation matrix of the +200 feature set and paired each feature with the feature it's the least correlated with. Then we applied a bunch of aggregations on each pair and it resulted in pretty strong features.

So we ended up with two feature sets with +1000 features each.

FEATURE SELECTION

For feature selection we did some manual feature selection based on the features importance feedback we got from lgb. Then we used this (simple yet effective) method here for some further filtering. What it basically does is remove:

Features with a high percentage of missing values according to a threshold Collinear (highly correlated) features Features with zero importance in a tree-based model Features with low importance Features with a single unique value The CV score got better in both feature sets.

After this, our best models scored around CV:3.639X LB:3.682

MODELS

We used different models for training: LightGBM / Xgboost / H2oRF / H2oGBM,also we tried a couple of NN architectures.

STACKING

We stacked around 32 models using linear regression. Our models were well varied that it yielded a score of CV:3.630X LB :3.675

POST PROCESSING

During the last day, we focused on doing some post processing and this is how we cherrypicked our outliers.

we carefully designed a Classifying module that combined four different classifers and multiply the regression value.

SUBMISSIONS

Finally we chose:

A model without post processing (Our best stacking sub) LB:3.675 and Private LB: 3.610

A model with post processing: LB:3.666 and Private:3.599

Thank you for sparing the time to read this.

And remember to always trust CV :D