Model description

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Intended uses & limitations

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Training Procedure

Hyperparameters

The model is trained with below hyperparameters.

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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.

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:

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Model Card Contact

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Citation

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BibTeX:

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