Model description

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Intended uses & limitations

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Training Procedure

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Hyperparameters

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Hyperparameter	Value
estimators	[('rf', RandomForestClassifier(random_state=12345)), ('lr', LogisticRegression(max_iter=1000, random_state=12345)), ('sgd', SGDClassifier(random_state=12345)), ('knn', KNeighborsClassifier()), ('ada', AdaBoostClassifier(random_state=12345))]
flatten_transform	True
n_jobs
verbose	False
voting	hard
weights
rf	RandomForestClassifier(random_state=12345)
lr	LogisticRegression(max_iter=1000, random_state=12345)
sgd	SGDClassifier(random_state=12345)
knn	KNeighborsClassifier()
ada	AdaBoostClassifier(random_state=12345)
rf__bootstrap	True
rf__ccp_alpha	0.0
rf__class_weight
rf__criterion	gini
rf__max_depth
rf__max_features	sqrt
rf__max_leaf_nodes
rf__max_samples
rf__min_impurity_decrease	0.0
rf__min_samples_leaf	1
rf__min_samples_split	2
rf__min_weight_fraction_leaf	0.0
rf__monotonic_cst
rf__n_estimators	100
rf__n_jobs
rf__oob_score	False
rf__random_state	12345
rf__verbose	0
rf__warm_start	False
lr__C	1.0
lr__class_weight
lr__dual	False
lr__fit_intercept	True
lr__intercept_scaling	1
lr__l1_ratio
lr__max_iter	1000
lr__multi_class	deprecated
lr__n_jobs
lr__penalty	l2
lr__random_state	12345
lr__solver	lbfgs
lr__tol	0.0001
lr__verbose	0
lr__warm_start	False
sgd__alpha	0.0001
sgd__average	False
sgd__class_weight
sgd__early_stopping	False
sgd__epsilon	0.1
sgd__eta0	0.0
sgd__fit_intercept	True
sgd__l1_ratio	0.15
sgd__learning_rate	optimal
sgd__loss	hinge
sgd__max_iter	1000
sgd__n_iter_no_change	5
sgd__n_jobs
sgd__penalty	l2
sgd__power_t	0.5
sgd__random_state	12345
sgd__shuffle	True
sgd__tol	0.001
sgd__validation_fraction	0.1
sgd__verbose	0
sgd__warm_start	False
knn__algorithm	auto
knn__leaf_size	30
knn__metric	minkowski
knn__metric_params
knn__n_jobs
knn__n_neighbors	5
knn__p	2
knn__weights	uniform
ada__algorithm	deprecated
ada__estimator
ada__learning_rate	1.0
ada__n_estimators	50
ada__random_state	12345

Model Plot

VotingClassifier(estimators=[('rf', RandomForestClassifier(random_state=12345)),('lr',LogisticRegression(max_iter=1000,random_state=12345)),('sgd', SGDClassifier(random_state=12345)),('knn', KNeighborsClassifier()),('ada', AdaBoostClassifier(random_state=12345))])

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Evaluation Results

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How to Get Started with the Model

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Model Card Authors

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Citation

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BibTeX:

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citation_bibtex

to be done

get_started_code

None

model_card_authors

Syreeta, Shraddha, Sravani, Sadhana, Ranjitha

limitations

Not handling logs

model_description

Failure prediction and remediation