Model description
[More Information Needed]
Intended uses & limitations
[More Information Needed]
Training Procedure
Hyperparameters
The model is trained with below hyperparameters.
Click to expand
| Hyperparameter | Value |
|---|---|
| ccp_alpha | 0.0 |
| class_weight | |
| criterion | gini |
| max_depth | |
| max_features | |
| max_leaf_nodes | |
| min_impurity_decrease | 0.0 |
| min_samples_leaf | 1 |
| min_samples_split | 2 |
| min_weight_fraction_leaf | 0.0 |
| random_state | |
| splitter | best |
Model Plot
The model plot is below.
DecisionTreeClassifier()Please rerun this cell to show the HTML repr or trust the notebook.
DecisionTreeClassifier
DecisionTreeClassifier()
## Evaluation Results
You can find the details about evaluation process and the evaluation results.
| Metric | Value |
|---|
How to Get Started with the Model
Use the code below to get started with the model.
import joblib
import json
import pandas as pd
clf = joblib.load(example.pkl)
with open("config.json") as f:
config = json.load(f)
clf.predict(pd.DataFrame.from_dict(config["sklearn"]["example_input"]))
Model Card Authors
This model card is written by following authors:
[More Information Needed]
Model Card Contact
You can contact the model card authors through following channels: [More Information Needed]
Citation
Below you can find information related to citation.
BibTeX:
[More Information Needed]