Summary of 2_DecisionTree

<< Go back

Decision Tree

n_jobs: -1
criterion: entropy
max_depth: 4
num_class: 4
explain_level: 0

Validation

validation_type: split
train_ratio: 0.75
shuffle: True
stratify: True

Optimized metric

logloss

Training time

1.3 seconds

Metric details

	1	2	3	4	accuracy	macro avg	weighted avg	logloss
precision	0.663255	0	0.41997	0.590541	0.607067	0.418441	0.578562	0.909709
recall	0.754595	0	0.281625	0.620914	0.607067	0.414284	0.607067	0.909709
f1-score	0.705983	0	0.337158	0.605347	0.607067	0.412122	0.589295	0.909709
support	10391	655	3913	9974	0.607067	24933	24933	0.909709

Confusion matrix

	Predicted as 1	Predicted as 2	Predicted as 3	Predicted as 4
Labeled as 1	7841	0	485	2065
Labeled as 2	158	0	243	254
Labeled as 3	836	0	1102	1975
Labeled as 4	2987	0	794	6193

Learning curves

Learning curves

<< Go back