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Model description

This model was created following the instructions in the following Kaggle notebook:

https://www.kaggle.com/code/michalbrezk/xgboost-classifier-and-hyperparameter-tuning-85

The possible classified predictions are: 'Non liver patient', 'Liver patient'

The predictors are: age, gender, total_bilirubin, alkaline_phosphotase, alamine_aminotransferase, albumin_and_globulin_ratio

Intended uses & limitations

This model follows the limitations of the Apache 2.0 license.

Hyperparameters

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Hyperparameter Value
bootstrap False
ccp_alpha 0.0
class_weight
criterion gini
max_depth
max_features sqrt
max_leaf_nodes
max_samples
min_impurity_decrease 0.0
min_samples_leaf 1
min_samples_split 2
min_weight_fraction_leaf 0.0
n_estimators 100
n_jobs
oob_score False
random_state 123
verbose 0
warm_start False

Model Plot

ExtraTreesClassifier(random_state=123)
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Evaluation Results

Metric Value
accuracy 0.836538
f1 score 0.836538

Model description/Evaluation Results/Classification report

index precision recall f1-score support
Liver patient 0.814159 0.87619 0.844037 105
Non liver patient 0.863158 0.796117 0.828283 103
macro avg 0.838659 0.836153 0.83616 208
weighted avg 0.838423 0.836538 0.836236 208

How to Get Started with the Model

To use the AI model run the following code on Google Colab:

https://colab.research.google.com/drive/1OKyEMTrrBqjdc9_3wgnn_ZHaRYMmr7mx?usp=sharing

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