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
Click to expand| 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)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.ExtraTreesClassifier
ExtraTreesClassifier(random_state=123)
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