Summary of 8_LightGBM

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LightGBM

• n_jobs: 6
• objective: multiclass
• num_leaves: 63
• learning_rate: 0.1
• feature_fraction: 0.5
• bagging_fraction: 1.0
• min_data_in_leaf: 10
• metric: custom
• custom_eval_metric_name: f1
• num_class: 6
• explain_level: 1

Validation

• validation_type: kfold
• k_folds: 5
• shuffle: True
• stratify: True
• random_seed: 42

Optimized metric

f1

Training time

43.7 seconds

Metric details

	0	1	2	3	4	5	accuracy	macro avg	weighted avg	logloss
precision	0.96942	0.967928	0.975835	0.98922	0.995628	0.998437	0.982423	0.982745	0.982529	0.0624095
recall	0.99147	0.966069	0.968367	0.991255	0.996173	0.976566	0.982423	0.98165	0.982423	0.0624095
f1-score	0.980321	0.966998	0.972087	0.990236	0.995901	0.987381	0.982423	0.982154	0.982427	0.0624095
support	2462	1562	1960	1944	1829	1963	0.982423	11720	11720	0.0624095

Confusion matrix

	Predicted as 0	Predicted as 1	Predicted as 2	Predicted as 3	Predicted as 4	Predicted as 5
Labeled as 0	2441	5	1	12	1	2
Labeled as 1	2	1509	45	3	2	1
Labeled as 2	12	45	1898	3	2	0
Labeled as 3	16	0	0	1927	1	0
Labeled as 4	4	0	1	2	1822	0
Labeled as 5	43	0	0	1	2	1917

Learning curves

Permutation-based Importance

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Precision Recall Curve

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