06-00-ACA-Evaluation.pdf

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This document discusses evaluation metrics for machine learning models. It covers classification metrics like accuracy, precision, recall and F1 score. Regression metrics like MAE, MSE and R2 are also discussed. The document stresses the importance of proper evaluation methodology, including using representative test sets, comparing to baselines, and testing for statistical significance. Good practices outlined include task-driven not algorithm-driven evaluation and using established datasets and metrics.

Technology

Introduction to Audio Content Analysis
module 6.0: evaluation and metrics
alexander lerch

overview intro classification regression summary
introduction
overview
corresponding textbook section
chapter 6
lecture content
• evaluation methodology
• good practices
• metrics
learning objectives
• design proper evaluation setups for machine learning algorithms
• list relevant metrics for different machine learning models
module 6.0: evaluation and metrics 1 / 9

overview intro classification regression summary
evaluation
introduction
without proper evaluation, there is no way to say whether a system works
typical mistakes in evaluation
1 non-representative test set
1 small, too homogeneous, ...
2 tuning system parameters with the test set (explicitly or implicitly)
3 using misleading evaluation procedures and metrics
module 6.0: evaluation and metrics 2 / 9

overview intro classification regression summary
evaluation
good practices 1/2
evaluation method unrelated to the specific implementation
• has to be task driven, not algorithm driven
• metrics should be unrelated to loss function
expectations clearly defined
• worst case performance (random)
• best case performance (oracle)
• realistic performance ⇒ baseline system
▶ Zero-R classifier
▶ standard approach
module 6.0: evaluation and metrics 3 / 9

overview intro classification regression summary
evaluation
good practices 2/2
comparability to state-of-the-art
• use of established datasets and identical data splits
• running existing systems on your data
increase reproducibility
• automate evaluation
• publish source code
test for statistical significance
module 6.0: evaluation and metrics 4 / 9

overview intro classification regression summary
classification metrics
introduction
possible outcomes of two class problem (positive and negative):
• TP: Positives correctly identified as Positives,
• TN: Negatives correctly identified Negatives,
• FP: Negatives incorrectly identified Positives, and
• FN: Positives incorrectly identified Negatives.
visualization: confusion matrix
Predicted
Positive Negative Σ
GT
Positive
TP
True Positives
FN
False Negatives
TP+FN
# of GT Positives
Negative
FP
False Positives
TN
True Negatives
FP+TN
# of GT Negatives
Σ
TP+FP
# of Predicted Positives
TN+FN
# of Predicted Negatives
TP+TN
# of True Predictions
module 6.0: evaluation and metrics 5 / 9

overview intro classification regression summary
classification metrics
accuracy and f-measure
accuracy: how many predictions are
accurate
macro accuracy: averaged over
classes (not observations)
precision: how many predicted
positives are correct
recall: how many ground truth
positives correctly predicted
f-measure: combines precision and
recall
Acc =
TP + TN
TP + TN + FP + FN
module 6.0: evaluation and metrics 6 / 9

overview intro classification regression summary
classification metrics
area under curve
module 6.0: evaluation and metrics 7 / 9
matlab
source:
plotROC.m

overview intro classification regression summary
regression metrics
mae, mse, R2
goal: measure deviation
mean absolute error
mean squared error
coefficient of determination
MAE =
1
R
X
∀r
|y(r) − ŷ(r)|
module 6.0: evaluation and metrics 8 / 9

overview intro classification regression summary
summary
lecture content
evaluation
• system development without evaluation is meaningless
• data and method need to be carefully selected
• metrics need to reflect the sucess of the system
classification metrics
• accuracy and macro accuracy
• precision, recall, and f-measure
• AUC
regression metrics
• MAE and MSE
• coefficient of determination
module 6.0: evaluation and metrics 9 / 9

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