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
criterion	friedman_mse
init
learning_rate	0.1
loss	log_loss
max_depth	3
max_features
max_leaf_nodes
min_impurity_decrease	0.0
min_samples_leaf	1
min_samples_split	2
min_weight_fraction_leaf	0.0
n_estimators	100
n_iter_no_change
random_state
subsample	1.0
tol	0.0001
validation_fraction	0.1
verbose	0
warm_start	False

Model Plot

The model plot is below.

GradientBoostingClassifier()

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Evaluation Results

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

Metric	Value
accuracy	0.996979
precision	0.996988
recall	0.996875
f1-score	0.996913

How to Get Started with the Model

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Model Card Authors

This model card is written by following authors:

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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:

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ModelDescription

The model is a product of academic work by Ümit Işıkdağ,more info on me Ümit Işıkdağ.

GettingStarted

!pip install skops # @only first run
#prepare train/test sets (X_train, X_test, y_train, y_test) as usual
import skops.hub_utils as hub_utils
from sklearn.metrics import accuracy_score
y_pred = hub_utils.get_model_output('uisikdag/simple_clasi_dataexp2weka', X_test)
accuracy =accuracy_score(y_test, y_pred)

ModelCardAuthor

Ümit Işıkdağ@2023

ModelLimitations

This model is not ready to be used in production.

CitationBibtex

bibtex @inproceedings{...,year={2020}}

ConfusionMatrix