Summary of 6_LightGBM_SelectedFeatures

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LightGBM

• n_jobs: 6
• objective: multiclass
• num_leaves: 63
• learning_rate: 0.2
• 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

32.5 seconds

Metric details

	0	1	2	3	4	5	accuracy	macro avg	weighted avg	logloss
precision	0.97483	0.966581	0.971487	0.99075	0.997806	0.997925	0.983191	0.98323	0.983266	0.0588952
recall	0.991064	0.962868	0.973469	0.99177	0.994533	0.980132	0.983191	0.982306	0.983191	0.0588952
f1-score	0.98288	0.964721	0.972477	0.99126	0.996166	0.988949	0.983191	0.982742	0.9832	0.0588952
support	2462	1562	1960	1944	1829	1963	0.983191	11720	11720	0.0588952

Confusion matrix

	Predicted as 0	Predicted as 1	Predicted as 2	Predicted as 3	Predicted as 4	Predicted as 5
Labeled as 0	2440	8	2	10	0	2
Labeled as 1	2	1504	53	2	1	0
Labeled as 2	7	43	1908	2	0	0
Labeled as 3	13	1	0	1928	1	1
Labeled as 4	5	0	1	3	1819	1
Labeled as 5	36	0	0	1	2	1924

Learning curves

Permutation-based Importance

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Precision Recall Curve

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