Data format | Data preparation | Node preparation rules for {tabnet} models | Avoid factor predictors | Avoid column name collision with reserved {data.tree} names | Avoid column named level_* to avoid collision with output data.tree names | Ensure the last hierarchy of the tree is the observation id | Ensure there is a root level in the hierarchy | Ensure there is no missing values in the hierarchical classes | Model building | Data set split | Model diagnostic | Model inference

Experiments | Datasets | Syn2 | Syn 4

Motivation | Missing-data dataset creation | Ames missings understanding | While keeping some room for freedom | Ames with missing data | Model pretraining | Variable importance with raw ames dataset | Variable importance with ames_missing dataset | Model training | Conclusion

Data preprocessing | Self-supervised training step | Continuing training with supervised task | Comparing against a model without pretraining

Data Visualization | Recall pinned dataset | Convert into time-series | Naive imputation | Gaps visualization | Conclusion

Introduction | How imbalance is my problem ? | Solutions to improve imbalanced classification models | Using the AUC metric and pr_curve() plots | Case-weight | ROC_AUM loss | All together

Data Collection | Use pins storage | All-in-one in a script | MacOS / Linux platform | MS Windows platform

APsystems device coverage | Continuous local mode for your micro-inverter | Design | Inverter setup