scikit-learn/cancer-prediction-trees

Model description

This is a Decision Tree Classifier trained on breast cancer dataset and pruned with CCP.

Intended uses & limitations

This model is trained for educational purposes.

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_impurity_split
min_samples_leaf	1
min_samples_split	2
min_weight_fraction_leaf	0.0
random_state	0
splitter	best

Model Plot

The model plot is below.

DecisionTreeClassifier

DecisionTreeClassifier(random_state=0)

Evaluation Results

You can find the details about evaluation process and the evaluation results.

Metric	Value
accuracy	0.937063
f1 score	0.937063

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(model.pkl)
with open("config.json") as f:
    config = json.load(f)
clf.predict(pd.DataFrame.from_dict(config["sklearn"]["example_input"]))

Additional Content

Feature Importances

Tree Splits

Confusion Matrix