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Metrics for Anomaly Detection: Precision, Recall, Rank Power

Added: 2026-08-07

Deep Dive

Fundamental understanding of anomaly detection concepts and how they differ from standard classification tasks.

HIP16-TALK Machine learning based ML techniques

HIP16-TALK Machine learning based ML techniques

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Anomaly detection differs fundamentally from standard classification problems. While classification identifies known categories (apples vs oranges) from labeled examples, anomaly detection must identify novel, previously unseen attacks. Five key challenges exist: high cost of false positives (blocking legitimate users harms experience), lack of clean training data (labeling traffic as normal/malicious is ambiguous), difficulty interpreting results (adaptive models make explanations complex), evaluation challenges (outdated datasets don't reflect modern traffic), and adversarial impact (attackers actively try to bypass systems).

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[핵심 머신러닝] Anomaly Detection (개요, 확률분포기반)

[핵심 머신러닝] Anomaly Detection (개요, 확률분포기반)

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Anomaly detection deals with highly imbalanced datasets where normal data constitutes the majority (e.g., 99%) and anomalies a small minority (e.g., 1%). Applying supervised learning to such data creates models biased toward the majority class, resulting in high accuracy but poor anomaly detection. The solution is unsupervised learning, which uses only normal data to learn what constitutes 'normal' behavior. Anomaly detection is fundamentally a one-class classification problem where the model learns to recognize normal data and identifies anything outside this learned pattern as an anomaly. The model creates a boundary encompassing normal data, and points outside this boundary are flagged as anomalies.

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Aula 10 novelty detection

Aula 10 novelty detection

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Novelty detection (one-class classification/anomaly detection) differs fundamentally from traditional multi-class classification. While multi-class classification separates different known categories (e.g., apples vs pears), novelty detection models what is 'normal' and identifies deviations as 'novel' or 'anomalous'. Training data contains only normal examples, and the model learns to recognize anything outside this pattern. Key terminology includes: target data (normal training examples), anomalies (novelty), and outliers. The practical goal is to detect previously unseen conditions, such as volcanic eruptions that haven't occurred during training. This approach is essential when abnormal patterns are rare or impossible to observe during model development.

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Прикладные задачи анализа данных, лекция 13 — поиск аномалий

Прикладные задачи анализа данных, лекция 13 — поиск аномалий

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Anomaly detection is a one-class classification problem where the goal is to identify unusual patterns that deviate from normal behavior. Key applications include fraud detection in banking (unauthorized transactions) and ride-sharing platforms (fake rides, location manipulation). The fundamental challenge is class imbalance, where normal examples vastly outnumber anomalies, making model training difficult. Additionally, traditional classification approaches only detect anomalies similar to training data, failing to identify novel attack patterns. Effective anomaly detection requires novelty detection—identifying any deviation from normal behavior, regardless of whether it matches known patterns.

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ai.bythebay.io: Mike Tamir, The Advantages of Deep Learning with Recurrent Neural Networks

ai.bythebay.io: Mike Tamir, The Advantages of Deep Learning with Recurrent Neural Networks

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Anomaly detection involves identifying data points that differ from core patterns. Key distinctions include: supervised vs unsupervised learning (fundamentally unsupervised but often integrates supervised techniques), multivariate vs univariate target analysis (multivariate examines relationships between features simultaneously), and static vs time series data (static distribution analysis fails for periodic time series where anomalies break temporal patterns). These foundational concepts determine how anomalies are defined and detected.

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Deep Dive

Familiarity with the Confusion Matrix, including True Positives, False Positives, True Negatives, and False Negatives.

Confusion Matrix Simplified for Beginners 2022 | Machine Learning Tutorial | SCALER

Confusion Matrix Simplified for Beginners 2022 | Machine Learning Tutorial | SCALER

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The confusion matrix contains four key components: True Negatives (TN) - correct predictions where the model predicted 0 (not leaving) and the actual outcome was 0; True Positives (TP) - correct predictions where the model predicted 1 (leaving) and the actual outcome was 1; False Positives (FP) - incorrect predictions where the model predicted 1 but the actual outcome was 0 (false alarm); False Negatives (FN) - incorrect predictions where the model predicted 0 but the actual outcome was 1 (most critical error). In the example, TN = 730, TP = 150, FP = 40, FN = 80.

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Introduction to the Confusion Matrix in Classification | Data Science in Minutes

Introduction to the Confusion Matrix in Classification | Data Science in Minutes

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To understand a confusion matrix, one must first grasp four key terms: True Positive (TP) occurs when the target class (positive) is correctly predicted as itself; True Negative (TN) occurs when the non-target class (negative) is correctly predicted as itself; False Positive (FP) occurs when the actual class is negative but predicted as positive; False Negative (FN) occurs when the actual class is positive but predicted as negative. These terms track whether predictions match reality.

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Mengenal Confusion Matrix - Part 1

Mengenal Confusion Matrix - Part 1

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The four key terms in confusion matrix are: (1) True Positive (TP): correctly predicted as positive, (2) True Negative (TN): correctly predicted as negative, (3) False Positive (FP): incorrectly predicted as positive (actual negative), (4) False Negative (FN): incorrectly predicted as negative (actual positive).

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👉 Confusion Matrix in 5 Minutes 🔥 | Class 10 AI 417 CBSE Exam 2025–26

👉 Confusion Matrix in 5 Minutes 🔥 | Class 10 AI 417 CBSE Exam 2025–26

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The four components of a confusion matrix are: True Positive (TP) - when both actual and predicted values are positive and match; True Negative (TN) - when both actual and predicted values are negative and match; False Positive (FP) - when actual is negative but prediction is positive; False Negative (FN) - when actual is positive but prediction is negative. Positive and negative labels are determined by the prediction column: if prediction is 'yes' or '1', it's positive; if 'no' or '0', it's negative. The matrix structure has predictions on the X-axis and actual values on the Y-axis.

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Confusion Matrix made easy

Confusion Matrix made easy

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The four components of a confusion matrix are: (1) True Positive (TP): actual outcome is positive and prediction is positive; (2) False Positive (FP): actual outcome is negative but prediction is positive; (3) False Negative (FN): actual outcome is positive but prediction is negative; (4) True Negative (TN): actual outcome is negative and prediction is negative.

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Deep Dive

The concept of class imbalance and why standard accuracy is a misleading metric for rare event detection.

CMPS 460 | Machine Learning | S22 | Session 5.f | Practical Issues (Debugging Learning Algorithms)

CMPS 460 | Machine Learning | S22 | Session 5.f | Practical Issues (Debugging Learning Algorithms)

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Imbalanced data occurs when one class is vastly underrepresented (e.g., 1-5 fraudulent transactions among 1 million legitimate ones). Standard accuracy is misleading because predicting all examples as the majority class achieves near-perfect accuracy while failing to detect rare events. This demonstrates why accuracy alone cannot evaluate models on imbalanced datasets—specialized evaluation metrics and approaches are required to properly assess model performance on rare event detection tasks.

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mlcourse.ai. Lecture 5. Part 2. Classification metrics. Theory

mlcourse.ai. Lecture 5. Part 2. Classification metrics. Theory

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Accuracy fails dramatically when dealing with imbalanced datasets where one class is extremely rare. For example, if only 1% of clients actually pay in a mobile application, a naive baseline classifier that always predicts 'not paying' achieves 99% accuracy. A sophisticated model achieving 95% accuracy appears impressive but may actually perform no better than random guessing. This occurs because accuracy gives equal weight to all classes regardless of their frequency. In such cases, accuracy cannot distinguish between a useless model and a useful one, making it an unreliable metric for rare event prediction tasks.

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Module #7: Classification

Module #7: Classification

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Prevalence refers to the proportion of cases in the total population. When dealing with rare events or highly imbalanced datasets, standard accuracy metrics become misleading because a classifier can achieve high accuracy simply by predicting the majority class. For example, if only 2% of patients have a disease, always predicting 'no disease' yields 98% accuracy despite being completely uninformative. This necessitates using alternative evaluation metrics that account for class distribution.

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Class Imbalance problem | Classification | Machine Learning

Class Imbalance problem | Classification | Machine Learning

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Class imbalance significantly degrades model performance by causing bias toward the majority class. Standard accuracy metric becomes misleading in imbalanced scenarios—a model can achieve 99.85% accuracy while failing to identify any minority class cases. For instance, if 99.5% of a population is healthy and 0.5% has a disease, predicting everyone as healthy achieves high accuracy but fails completely on the minority class. Alternative evaluation metrics are essential: precision, recall, F1-score, and ROC-AUC provide comprehensive performance assessment by considering both true positive and false positive rates. Understanding which class is minority versus majority is crucial for selecting appropriate evaluation strategies and handling techniques.

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Seleção de modelos de classificação e desbalanceamento

Seleção de modelos de classificação e desbalanceamento

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Accuracy (1 - proportion of errors) is a poor metric for imbalanced classification problems. In a rare disease scenario where Y=1 is very rare, a classifier that predicts everyone as Y=0 (healthy) will have very high accuracy but will fail to identify any actual cases. This is why accuracy alone is misleading for imbalanced datasets. A confusion matrix provides a complete picture by showing True Negatives, False Negatives, False Positives, and True Positives.

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Deep Dive

Basic knowledge of thresholding and scoring in machine learning model outputs.

Machine Learning #05 - Klassificering - teori

Machine Learning #05 - Klassificering - teori

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Classification models output probability scores that must be converted to decisions using thresholds. The standard threshold is 0.5, but optimal thresholds depend on the problem's cost structure. Lowering thresholds increases true positives but also false positives; raising thresholds reduces false positives but may miss true positives. The confusion matrix summarizes model performance with four outcomes: True Positive (correct positive prediction), True Negative (correct negative prediction), False Positive (false alarm), and False Negative (miss). True and False refer to model correctness, while Positive and Negative refer to predictions. The key question is which type of error is more costly in the specific application.

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S15 Basic ML 1 Rajdeep Chatterjee

S15 Basic ML 1 Rajdeep Chatterjee

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Trained models output scores (probabilities) for each data point, not just class labels. The operating threshold determines how these scores are converted to class predictions. Higher thresholds result in purer predictions but may miss some true signals, while lower thresholds capture more signals but allow more background. The ROC curve is generated by scanning through all possible threshold values, calculating TPR and FPR for each. The model parameters are fixed after training; only the threshold varies during ROC curve generation. Data should be randomly split into training and test sets after shuffling to ensure unbiased evaluation.

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Evaluation Metrics - Quick Overview

Evaluation Metrics - Quick Overview

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Machine learning models produce continuous scores (often 0-1 range) representing confidence or risk, not binary predictions. Thresholds convert these scores to 0/1 classifications. The confusion matrix (True Positives, True Negatives, False Positives, False Negatives) forms the foundation for all threshold-based metrics. Thresholds are arbitrary and not comparable across models—a 0.6 threshold in one model doesn't mean the same in another. This conversion process is essential for calculating accuracy, precision, and recall.

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EP6(สุดท้าย) สอนการใช้งาน Colab กับ Iris Dataset ด้วย K-Nearest Neighbor - Part 4

EP6(สุดท้าย) สอนการใช้งาน Colab กับ Iris Dataset ด้วย K-Nearest Neighbor - Part 4

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Threshold (or threshold) is a configurable value that determines how a model's prediction score is converted into a class label. For example, with threshold = 0.5, predictions above 0.5 are classified as positive and below as negative. Adjusting the threshold affects the trade-off between True Positive Rate and False Positive Rate. Higher thresholds reduce False Negatives but increase False Positives, while lower thresholds do the opposite. The optimal threshold depends on the specific application and the relative costs of different types of errors.

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Çok-Etiketli Sınıflandırmada Başarım Ölçümü: Precision Recall F1 ölçütleri 1/3

Çok-Etiketli Sınıflandırmada Başarım Ölçümü: Precision Recall F1 ölçütleri 1/3

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This section explains how machine learning models output prediction scores (probabilities between 0 and 1) for each class. The presenter demonstrates how true labels (ground truth) are compared against these prediction scores. A critical concept introduced is threshold selection: converting continuous scores into binary predictions (0 or 1) by applying a cutoff value (e.g., 0.5). Scores above the threshold become positive predictions, while scores below become negative. This threshold determines how confident the model must be before making a positive prediction.

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Model Evaluation

Precision & Recall

Metrics Example

Rank Power Use

Rank Calculation

Metric Selection

Model Evaluation

0:00

Playing Section

1
Identify key metrics for anomaly detection model performance.
2
Learn the process for calculating these performance metrics.

Deep Dive

Evaluating models using advanced curve metrics, such as Precision-Recall (PR) Curves and Area Under the Precision-Recall Curve (AUPRC).

Precision-Recall Curves and AUPRC | Confusion Matrix Metrics Part 9 | Machine Learning

Precision-Recall Curves and AUPRC | Confusion Matrix Metrics Part 9 | Machine Learning

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Precision-Recall Curves plot precision (correct positive predictions / total positive predictions) on the y-axis against recall (correct positive predictions / total actual positives) on the x-axis, with the Area Under the Precision-Recall Curve (AUPRC) serving as a key metric for evaluating classification models, particularly in imbalanced datasets where ROC curves can be misleading; a perfect classifier achieves AUPRC = 1, while the baseline AUPRC equals the proportion of positive samples in the dataset.

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Exploring Machine Learning Classifiers for Accurate Heart Attack Prediction| Part 1

Exploring Machine Learning Classifiers for Accurate Heart Attack Prediction| Part 1

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To evaluate model performance: (1) Predict probabilities using predict_proba(), (2) Calculate Precision-Recall Curve using precision_recall_curve() function, (3) Calculate ROC Curve using roc_curve() function, (4) Calculate Area Under the Curve (AUC) for both curves. AUC ranges from 0 to 1, with higher values indicating better performance. For balanced class distributions, ROC Curve is preferred; for imbalanced datasets, Precision-Recall Curve is more appropriate. Decision Tree achieved PR Curve AUC=0.99 and ROC Curve AUC=0.98, Stochastic Gradient Descent achieved PR Curve AUC=0.90 and ROC Curve AUC=0.83, Extra Trees achieved ROC Curve AUC=0.98.

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How to evaluate ML models | Evaluation metrics for machine learning

How to evaluate ML models | Evaluation metrics for machine learning

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The PR (Precision-Recall) curve compares recall values at different precision levels, showing the trade-off between precision and recall. A good PR curve should appear in the top-right corner indicating high precision and high recall. The ROC (Receiver Operating Characteristic) curve compares the true positive rate against the false positive rate. Both curves help understand model performance, and the Area Under the Curve (AUC) value should be as high as possible, indicating better model performance.

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Lecture 11 | ROC Curve | Precision Recall Curve | Area Under Curve | AUC | Explanation

Lecture 11 | ROC Curve | Precision Recall Curve | Area Under Curve | AUC | Explanation

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This section introduces Precision-Recall Curve as the preferred evaluation method for imbalanced datasets. Precision-Recall Curve plots Precision on the y-axis and Recall on the x-axis at multiple threshold values. Precision measures positive prediction quality (TP/(TP+FP)), which is more relevant when positive examples are rare. At threshold = 0, all points are predicted as positive (recall = 1, precision = 0). At threshold = 1, all points are predicted as negative (recall = 0, precision = 0). An ideal model should maintain high recall while increasing precision as threshold increases. AUPRC (Area Under the Precision-Recall Curve) is used to compare models for imbalanced datasets, with higher AUPRC indicating better performance at detecting positive classes.

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Mean Average Precision (mAP) Explained and PyTorch Implementation

Mean Average Precision (mAP) Explained and PyTorch Implementation

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Precision measures the fraction of correct predictions among all predictions made (TP/(TP+FP)), answering 'Of all predicted boxes, how many were correct?' Recall measures the fraction of actual targets detected (TP/(TP+FN)), answering 'Of all actual targets, how many were found?' These metrics are inversely related—increasing recall typically decreases precision. The precision-recall curve plots precision against recall at different confidence thresholds. Average precision is calculated as the area under this curve, providing a single scalar value summarizing model performance for a specific class.

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Deep Dive

Applying cost-sensitive learning and tailoring the F-beta score to weigh precision versus recall based on domain-specific costs (e.g., fraud vs. medical diagnosis).

Machine Learning: Testing and Error Metrics

Machine Learning: Testing and Error Metrics

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The F beta score generalizes the F1 score by allowing different weights on precision and recall through the beta parameter. When beta = 1, it equals the F1 score (equal emphasis). When beta < 1, precision is weighted more heavily (F0.5 score). When beta > 1, recall is weighted more heavily (F2 score). For credit card fraud detection, since missing fraud (false negative) is worse than false alarms (false positive), a higher beta (closer to recall) would be appropriate. The choice of beta depends on which type of error is more costly for the specific application.

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Ridge, Lasso & Logistic Regression | ML Algorithms Explained

Ridge, Lasso & Logistic Regression | ML Algorithms Explained

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Precision and recall often conflict: improving one reduces the other. For spam detection, precision matters (avoid false positives). For medical diagnosis, recall matters (avoid false negatives). The F-score balances them: Fβ = [(1+β²)PR]/[β²P + R]. β=1 gives F1 (equal weight). β<1 prioritizes precision; β>1 prioritizes recall. This framework allows tailoring evaluation to specific problem costs and requirements.

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ЮФУ -- 2025.12.11 -- Функция регрессии, разложение bias-variance-noise, выбор моделей

ЮФУ -- 2025.12.11 -- Функция регрессии, разложение bias-variance-noise, выбор моделей

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When classification costs are asymmetric (e.g., false negatives are more costly than false positives), the optimal classifier changes. Instead of maximizing P(C|X), we maximize w_c * P(C|X), where w_c are cost weights. For example, in medical testing where missing a disease is worse than false alarms, we would weight the positive class higher. This provides a principled way to incorporate domain-specific cost considerations into classifier design.

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Performance Metrics, Accuracy,Precision,Recall And F-Beta Score Explained In Hindi|Machine Learning

Performance Metrics, Accuracy,Precision,Recall And F-Beta Score Explained In Hindi|Machine Learning

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This section explores advanced techniques for balancing classification performance. The trade-off between precision and recall means improving one typically reduces the other, requiring domain-specific decisions. In email spam detection, false positives (missing important emails) and false negatives (spam slipping through) have different costs depending on context. In medical diagnosis, particularly cancer detection, false negatives are far more dangerous than false positives. The F-Beta score combines precision and recall using a weighted harmonic mean, with β controlling the balance—β=1 gives equal weight (F1 score), β>1 emphasizes recall, β<1 emphasizes precision. This allows customization for specific applications: F1 for balanced importance, higher β for life-critical applications, lower β for precision-critical scenarios like spam filtering.

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Introduction to Pattern Recognition and ML -- Lecture 2 - Winter 2024

Introduction to Pattern Recognition and ML -- Lecture 2 - Winter 2024

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Cost-sensitive learning allows assigning different costs to different types of errors. Instead of symmetric zero-one loss, asymmetric costs can be assigned where L(Ŷ=1|Y=0) ≠ L(Ŷ=0|Y=1). For example, in medical diagnosis, falsely diagnosing cancer (false positive) might have much higher cost than missing a cancer case (false negative). The optimal classifier then minimizes the expected cost rather than just the error count.

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Deep Dive

Exploring rank-based evaluation metrics like Normalized Discounted Cumulative Gain (NDCG) for prioritized anomaly investigation.

nDCG: the evaluation metric you've (probably) never heard of

nDCG: the evaluation metric you've (probably) never heard of

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nDCG is a ranking evaluation metric that measures how well a ranking model performs by rewarding both high relevance scores and early positioning in the ranking list; it calculates Discounted Cumulative Gain (DCG) for a given ranking and normalizes it by dividing by the Ideal DCG (the best possible ranking), where DCG applies exponential relevance weighting (2^relevance - 1) divided by logarithmic position discounting (log2(position+1)), ensuring that highly relevant items receive more credit when they appear near the top of the list.

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1.5 Rank-based Evaluation of a Search Engine - Normalized Discounted Cumulative Gain

1.5 Rank-based Evaluation of a Search Engine - Normalized Discounted Cumulative Gain

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Normalized Discounted Cumulative Gain (NDCG) is a ranking evaluation metric that normalizes the Discounted Cumulative Gain (DCG) of an actual search result list by dividing it by the DCG of an ideal ranking (where documents are ordered from highest to lowest relevance), enabling fair comparison of search engine performance across queries with different numbers of relevant results; NDCG values range from 0 to 1, where 1 indicates perfect ranking.

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Week 6: Recommender Systems - Part 4: Evaluation of Recommender Systems

Week 6: Recommender Systems - Part 4: Evaluation of Recommender Systems

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NDCG extends evaluation to account for item positions by first calculating DCG (Discounted Cumulative Gain), which sums relevance values divided by log(position+1) for each position. Then, Ideal DCG calculates what DCG would be if items were perfectly ordered by relevance. Finally, NDCG normalizes by dividing DCG by Ideal DCG. For example, a recommendation list might have DCG=1.56 and Ideal DCG=2.13, resulting in NDCG=0.73. Higher NDCG indicates better ranking quality.

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СПбГУ -- 2023-02-20 -- Обучение ранжированию: метрики, RankNet, LambdaRank, MART, LambdaMART

СПбГУ -- 2023-02-20 -- Обучение ранжированию: метрики, RankNet, LambdaRank, MART, LambdaMART

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nDCG (Normalized Discounted Cumulative Gain) normalizes the DCG score by dividing it by the ideal maximum DCG achievable if all relevant documents were perfectly ordered at the top positions. This normalization ensures the metric ranges from 0 to 1, making it easier to compare across different queries and datasets. A score of 1 indicates perfect ranking, while lower scores indicate room for improvement.

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17.6. Evaluation of retrieval model - Normalized discounted cumulative gain (NDCG)

17.6. Evaluation of retrieval model - Normalized discounted cumulative gain (NDCG)

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NDCG is an evaluation metric for information retrieval systems that addresses the limitation of binary relevance by incorporating graded relevance, where documents are assigned relevance scores (e.g., 0 for non-relevant, 1 for loosely relevant, 2 for moderately relevant, 3 for highly relevant). The metric operates through four components: (1) Gain - the relevance score of each document; (2) Cumulative Gain (CG) - the sum of gains up to a given rank; (3) Discounted Cumulative Gain (DCG) - CG adjusted by a logarithmic discount factor (log base 2 of rank+1) to penalize lower-ranked documents; (4) Normalization - dividing DCG by Ideal DCG (the maximum possible DCG if documents were perfectly ordered by relevance). This produces a normalized score between 0 and 1, where 1 indicates perfect performance matching an ideal retrieval system.

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Deep Dive

Hyperparameter tuning of unsupervised anomaly detection algorithms (like Isolation Forests or One-Class SVMs) using the studied evaluation metrics.

xAI for unsupervised ML in anomaly detection - LUCA use case by Alberto Barbado & Álvaro Sánchez

xAI for unsupervised ML in anomaly detection - LUCA use case by Alberto Barbado & Álvaro Sánchez

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One-Class SVM is an unsupervised anomaly detection algorithm that identifies dense clusters of normal data and builds decision boundaries around them. Key hyperparameters include nu (controlling margin maximization vs classification error) and rejection rate (balancing overfitting and underfitting). Grid search optimizes these parameters by evaluating trade-offs between keeping normal points far from boundaries (avoiding overfitting) and keeping anomalies close enough (avoiding underfitting). The objective function maximizes normal point distances while minimizing anomaly distances.

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[파이썬 머신러닝 실습] Anomaly Detection (LOF, iForest)

[파이썬 머신러닝 실습] Anomaly Detection (LOF, iForest)

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This tutorial demonstrates two anomaly detection algorithms—LOF (Local Outlier Factor) and Isolation Forest—using the Shuttle dataset. LOF identifies anomalies by comparing local density of data points, while Isolation Forest isolates anomalies through random partitioning. Both algorithms use evaluation metrics including accuracy, precision, recall, and F1 score, with Isolation Forest showing superior performance on this dataset. The tutorial covers implementation using both scratch code and sklearn packages, including hyperparameter optimization via grid search.

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One Class SVM for Anomaly Detection | Unsupervised Machine Learning

One Class SVM for Anomaly Detection | Unsupervised Machine Learning

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One-Class Support Vector Machine (One-Class SVM) is an unsupervised machine learning algorithm that learns the boundary of normal data points without requiring labeled training data, identifying data outside this boundary as anomalies; key hyperparameters include 'nu' (specifying the expected percentage of anomalies) and 'gamma' (kernel coefficient for RBF kernel), and the detection threshold can be customized by adjusting the score threshold to control the trade-off between true positive rate and false positive rate.

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Anomaly detection with Isolation Forests

Anomaly detection with Isolation Forests

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Isolation Forests are an unsupervised anomaly detection algorithm that uses isolation trees to identify outliers by measuring how easily data points can be isolated through random feature selection and partitioning; anomalous points are easier to isolate (requiring fewer partitions) than regular points, and the anomaly score is calculated using the formula 2^(-expected_path_length/c(m)), where lower scores indicate higher likelihood of being an anomaly.

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Anomaly detection (Isolation Forest, One-Class SVM)

Anomaly detection (Isolation Forest, One-Class SVM)

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This video explains two key unsupervised outlier detection algorithms: Isolation Forest, which identifies outliers by measuring how easily data points can be isolated through random binary partitioning (outliers require fewer splits), and One-Class SVM, which learns the distribution of normal data in high-dimensional space and classifies points outside the learned boundary as anomalies. Both methods detect data points that deviate significantly from the majority pattern without requiring labeled training data.

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Model Evaluation

Precision & Recall

Metrics Example

Rank Power Use

Rank Calculation

Metric Selection

Model Evaluation

0:00

Playing Section

1
Identify key metrics for anomaly detection model performance.
2
Learn the process for calculating these performance metrics.

196 views1011:08@JosephRivera517Original Release: 2021-08-08

This lesson explains three key metrics for evaluating anomaly detection algorithms: precision measures the proportion of correctly identified anomalies among all predicted anomalies (correctly identified anomalous points / total predicted anomalous points), recall measures the proportion of actual anomalies correctly identified (correctly identified anomalous points / total actual anomalous points), and rank power addresses the limitations of precision and recall by considering the ranks of suspected anomalies, calculated as (2 × sum of ranks of suspected anomalies) / (n × (n+1)), where n is the number of suspected anomalies. These metrics help data scientists choose the most effective algorithm for their specific anomaly detection tasks.

[00:00:00]at the end of the lesson you are expected to identify the metrics of measurement of anomaly detection algorithm follow the process of calculating the metrics of measurements and appreciate the use of the metrics in anomaly detection algorithms in applying our algorithm we always ask ourselves how does the algorithm that we have chosen perform because the performance really can affect the choice of the model that we're going to perform for future projects or future data sets of course in the process of this application you have to make some changes you have to make some kind of adjustments so as to conform to the new set of data and variables so that you can meet the demand of the business question at hand so this lesson is actually very important because this will give us the proper understanding of the metrics of measurement that we use for our anomaly detection algorithm but of course we have a lot of algorithms or what i mean is metrics to use for measuring the performance of our anomaly detection algorithm but so far these are the most famous ones so we have precision we do have recall and also we have rank power so we're going to have them one by one so first we're going to have precision what does a precision measure how do we arrive at the conclusion that a certain output given by our algorithm is really performing very much well how precise is it so basically precision answers the question how many of the data points that were predicted by the model to be abnormal were correctly identified to be anomalous by our model we can say for example that m okay it's not working well so i guess i have to change it okay so we have for example um m is greater than zero and this one represents all data points predicted to be anomalous or in other words this is the potential anomalous points out of these anomalous points how many are correctly identified to be unknown those by our algorithm so having these values now we can get the precision using this formula so we have the correctly identified anomalous points then we're going to divide that by the total number of data points that were identified by the model to be anomalous the higher the value would mean that the precision is really higher so it means that our model is performing very much well so when the value is 1 it means to say that all points were correctly identified by the model to be through outliers or nomados so after this identifying the position now we're going to ask ourselves has the model properly or correctly recalled the outliers or anomalous points so this question can be answered by this recall so what is this recall recall means how many out of these empty or true outliers the model correctly identified so basically we can do that by using this computation so just like this one when the value is equal to one then we could say that all true outliers are discovered by the algorithm but of course in the real world this one can be very difficult to arrive at so for us to be able to properly understand these two metrics position and recall we're going to have this example so this one concerns the credit card transaction fraud our data set has 1000 transactions and out of these 1000 transactions we consider 200 to be fraudulent so how do we get the precision and how do we get the recall so it's very easy we just have to plot these values in our formula so 200 so this one empty the ones presumed to be a nomados and we're going to divide that by 1000 and that gives us the precision of 20 or just 0.2 so what is the recall so we've said that the recall is the per person of the two outliers discovered by the model on top of the suspicious fraudulent transactions so we have in this case 200 divided by 200 and that is equal to 1 or 100 percent so this means that our model has perfectly identified all of the data points to be a nomados but then again in the real world this one does not happen as of this moment i want to tell you that the precision and recall actually have some kind of problems when it comes to identification because they are actually very much insufficient to completely identify the anomaly points or anomalous points so in short we can say that they are not very effective in identifying anomalies and why do we say this that using this precision recall does not make our algorithm really effective in identifying anomalies the reason for this is that for example when we have two algorithms used in anomaly detection the result can give us different numbers of anomalies so if we're going to look at this one here in this case we are using a larger number and of course this would give us a small amount so if we're going to use for example two algorithms in a certain situation for example in a credit card transaction one algorithm may identify one outlier to be the most suspicious one but in another algorithm this data point may be identified as the least suspicious here comes the problem in the use of precision and recall because we also have to remedy this kind of situation because not in all cases we may use the same number of data points remember this that we always or in most cases we use the 70 30 for the tweening and the test sets the remedy that we can offer is the use of around power and this rung power can answer the question in the situation that we have just illustrated so what is this one so basically the wrong power can be computed using this formula so we have ri which is equal to empty the number of data points which we have suspected to be anomalous and what we do is we multiply that to itself but then we add 1 over 2 and the summation of the ri so maybe you would want to ask me what is this ri all about so ri is actually the rank of the ith outlier in the sorted list sorted list of the most suspicious objects say for example when we have 10 suspicious data points or objects in our data set so we're going to rank them from 1 2 10 1 being the most suspicious point and the 10 being the least suspicious point so basically what we do here is just we rank the data points which were which are considered to be anomalous so for better understanding of this metric we're going to have this example so supposing we have 1000 data points and we have identified 100 data points to be anomalous so out of these 100 for our mts so what are these four so we have data point one did a point twenty five did a point seventy six and did a point ninety one so how do we get the position it's just very easy just like the number one what we do is we just divide four the empty over 100 or by 100 which is we have the suspicious data points and that gives us 0.04 so this one is not enough because we have to get the rp then so what's the rp just very easy we are just going to plug in our values so we have four and four plus one is five so four times five and we have already identified the positions or the ranks of the suspected or suspicious data points and the truly outliers are 1 position 1 or rank 1 rank 25 around 76 ranks 91.

[00:09:37]so what we do here is just we add them and then we multiply the result by 2 and so we have 30 or 20 divided by the result of this one and then we get 0.05 and so having this one is actually better than having precision and recall it's because we consider the ranks of the points which are considered to be the most auspicious and the least suspicious what is this for why do we have to study this basically having the proper understanding of the different metrics of performance or measurement is a great help to identify which algorithm is the best one to use apart from these metrics there are also other metrics to use for us to be able to identify the performance of the algorithms that we are using after all being said and done let's try this what are the metrics of measurement how do we calculate each of these metrics why do we have to know these metrics please write your answers in the comment down below so that we would be able to have a very rich interaction of ideas and we can learn from each other do you want to know more about this channel is click these cards we do have a lot of free data science courses for free like machine learning essentials deep learning mathematics and a lot more here you can always learn an upskill for free

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