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Smart Factory-LGaimers Hackathon

Defection Multiclass Classification, Prediction, Industrial AI Modeling

  • LG Aimers Hackathon - Smart Factory Classification

  • code, csv files, related materials

  • 02/17/2023(FRI):

    • preprocessing + GradientBoostingRegressor + GradientBoostingClassifier (X)
    • NO preprocessing + LGBM

  • 02/18/2023(SAT):

    • preprocessing: fillna with feature's column mean
    • append data for solving data imbalance
    • further Ensemble modeling is needed
    • Smart Factory Controling System: company's focus

  • 02/20/2023(MON):

    • preprocessing: fillna(0) + SMOTE oversampling
    • train_test set split
    • f1_score macro avg
    • Y_Quality ---> Y_Class prediction : f1_score not good
      • directly predicts Y_Class
    • DL: TabNet techniques
      • TabNetCalssifier modeling gets in stucked [ON GOING]
    • Ensemble Modeling
      • 2 level stacking

  • 02/21/2023(TUE):

    • params tuning: OPTUNA
    • XGBClassifier (predict directly Y_Class)

  • 02/24/2023(FRI): SCORE 0.72765!!!!!

    • DL: Sequential model + catBoostClassifier + optuna params optimization
    • 03/07 code & PPT submission

  • 02/25/2023(SAT): best output is not recalled

    • sequential model:
      • input layer, output layer(#neuron=#label)
      • num of hidden layers = 0 --> only capable of representing linear separable functions or decisions
        • when features are linearly correlated, it can be done by using ML.
        • but even in linear correlation, if neural networks are used, there is no need for any hidden layer

  • 02/26/2023(SUN): Tensorflow.Keras NN MLP (Sequential model) is not reproductable

    • sequential model + optuna
      • Tensorflow.keras: random --> familiar BUT not recalled
      • Torch: unfamiliar AND output is expected to be same
    • catBoost + GBC + voting classifier + optuna

  • 02/27/2023(MON): focusing on ML ensemble

    • catBoost + randomizedSearch + stacking ensemble
    • tried sequential model finally (CPU setting + seed fixing) but not recallable

  • 02/28/2023(TUE): FINISHED

    • VotingClassifier
    • based on each basic model's performance

  • RESULT

    • overfitting
    • Cross-Validation is VERY IMPORTANT

  • 03/01/2023(WED): feature distribution (DATA ANALYSIS), MLP in torch

    • feature distribution: imbalanced --> MinMaxScaler
    • MLP: code in torch
      • BUT still results are random
    • FEEDBACK:
      • tensorBoard --> automatical logging tool for parameters experiments etc.
      • decrease # neuron slowly
      • deep layers to make training hard --> prevent OVERFITTING

  • 03/04~03/06: PPT

  • 03/07: CODE & PPT Final Check

  • 03/22/2023(WED): DL modeling

    • LSTM, Sequential Model

  • 03/23/2023(THU): cross-validation + feature Importance

    • cross-validation score 0.550.6 is good (if cross validation > 0.70.8, then it seems like overfitting)
    • use ONLY import features(SHAP) with catBoostClassifier