Model description

[More Information Needed]

Intended uses & limitations

[More Information Needed]

Training Procedure

[More Information Needed]

Hyperparameters

Click to expand

Hyperparameter	Value
memory
steps	[('clf', RandomForestClassifier())]
verbose	False
clf	RandomForestClassifier()
clf__bootstrap	True
clf__ccp_alpha	0.0
clf__class_weight
clf__criterion	gini
clf__max_depth
clf__max_features	sqrt
clf__max_leaf_nodes
clf__max_samples
clf__min_impurity_decrease	0.0
clf__min_samples_leaf	1
clf__min_samples_split	2
clf__min_weight_fraction_leaf	0.0
clf__monotonic_cst
clf__n_estimators	100
clf__n_jobs
clf__oob_score	False
clf__random_state
clf__verbose	0
clf__warm_start	False

Model Plot

Pipeline(steps=[('clf', RandomForestClassifier())])

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.

Evaluation Results

Metric	Value
accuracy	0.807877
f1 score	0.729919
precision	0.665718
recall	0.807825

How to Get Started with the Model

[More Information Needed]

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]

eval_method

The model is evaluated using test split, on accuracy, precision, recall and f1.