Model Tuner
Getting Started
Welcome to Model Tuner’s Documentation!
Usage Guide
iPython Notebooks
Input Parameters
Key Methods and Functionalities
Helper Functions
Pipeline Management
Binary Classification
Multi-Class Classification
Regression
Performance Evaluation Metrics
Bootstrap Metrics
Caveats
Zero Variance Columns
Dependent Variable
Scaling Before Imputation
Column Stratification with Cross-Validation
Model Calibration
Using Imputation and Scaling in Pipeline Steps for Model Preprocessing
Caveats in Imbalanced Learning
Threshold Tuning Considerations
ElasticNet Regularization
CatBoost Training Parameters
About Model Tuner
GitHub Repository
Acknowledgements
Citing Model Tuner
Changelog
References
Model Tuner
Index
Index
B
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C
|
E
|
G
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M
|
R
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S
B
built-in function
check_input_type()
evaluate_bootstrap_metrics()
get_feature_names()
get_feature_selection_pipeline()
get_preprocessing_and_feature_selection_pipeline()
get_preprocessing_pipeline()
return_bootstrap_metrics()
return_metrics()
sampling_method()
C
check_input_type()
built-in function
E
evaluate_bootstrap_metrics()
built-in function
G
get_feature_names()
built-in function
get_feature_selection_pipeline()
built-in function
get_preprocessing_and_feature_selection_pipeline()
built-in function
get_preprocessing_pipeline()
built-in function
M
Model (built-in class)
R
return_bootstrap_metrics()
built-in function
return_metrics()
built-in function
S
sampling_method()
built-in function