Classification Report

TAN (Tree-Augmented Naive Bayes) — 400 samples · 3 classes

1.000
1.0000
Balanced Accuracy
Average per-class recall
100.00%
Accuracy
Fraction of all predictions that are correct: (TP+TN) / Total
100.00%
Balanced Acc
Average per-class recall, correcting for class imbalance
1.0000
Precision
Of predicted positives, how many are correct: TP / (TP+FP)
1.0000
Recall
Of actual positives, how many are found: TP / (TP+FN). Also called sensitivity
1.0000
F1 Score
Harmonic mean of precision and recall: 2·P·R / (P+R)
1.0000
MCC
Matthews Correlation Coefficient: balanced measure even with imbalanced classes. Range [-1, 1]
1.0000
Cohen κ
Agreement beyond chance between predictions and true labels. Range [-1, 1]
1.0000
AUC
Area Under the ROC Curve: probability that a random positive ranks above a random negative
0.0421
Log Loss
Negative log-likelihood of predicted probabilities. Lower is better

Confusion Matrix

ROC Curve

Precision-Recall Curve

Per-Class Metrics

Feature Importances (Top 6)

Class Distribution (Test Set)

Learned Structure — TANClassifier bayesian_network

Open interactive Bayesian network → Download structure (JSON) ↓

7 nodes · 11 edges

Markov blanket of target: [0, 1, 2, 3, 4, 5]

Edges

Y→0
Y→1
Y→2
Y→3
Y→4
Y→5
0→2
0→5
1→3
3→4
5→1

Per-Class Metrics

Class                 Precision     Recall         F1    Support
----------------------------------------------------------------
low                      1.0000     1.0000     1.0000        141
med                      1.0000     1.0000     1.0000        124
high                     1.0000     1.0000     1.0000        135