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Summary of 7_LightGBM_SelectedFeatures

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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

44.8 seconds

Metric details

0 1 2 3 4 5 accuracy macro avg weighted avg logloss
precision 0.973663 0.967076 0.968464 0.990246 0.997257 0.998444 0.982423 0.982525 0.982499 0.0523179
recall 0.991064 0.959027 0.971429 0.992284 0.993986 0.980642 0.982423 0.981405 0.982423 0.0523179
f1-score 0.982287 0.963034 0.969944 0.991264 0.995619 0.989463 0.982423 0.981935 0.982428 0.0523179
support 2462 1562 1960 1944 1829 1963 0.982423 11720 11720 0.0523179

Confusion matrix

Predicted as 0 Predicted as 1 Predicted as 2 Predicted as 3 Predicted as 4 Predicted as 5
Labeled as 0 2440 7 2 11 0 2
Labeled as 1 4 1498 58 1 1 0
Labeled as 2 11 44 1904 0 1 0
Labeled as 3 13 0 1 1929 1 0
Labeled as 4 6 0 1 3 1818 1
Labeled as 5 32 0 0 4 2 1925

Learning curves

Learning curves

Permutation-based Importance

Permutation-based Importance

Confusion Matrix

Confusion Matrix

Normalized Confusion Matrix

Normalized Confusion Matrix

ROC Curve

ROC Curve

Precision Recall Curve

Precision Recall Curve

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