Imbalanced binary classification with scikit-learn and PyTorch Lightning, on a large dataset of used cars. Comparing logistic regression, SVM and XGBoost trained with class weights, with a neural network trained with focal loss. Performing hyperparameter optimization with Optuna. Assessing model performances with classification metrics & a sensitivity analysis based on a business scenario.
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AhmetZamanis/UsedCarKicksClassification
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Imbalanced classification with scikit-learn and PyTorch Lightning.
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python
data-science
machine-learning
deep-learning
neural-network
scikit-learn
pytorch
xgboost
hyperparameter-optimization
classification
logistic-regression
support-vector-machines
sensitivity-analysis
stochastic-gradient-descent
class-weights
focal-loss
optuna
imbalanced-classification
pytorch-lightning
classification-metrics
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