#150
Автор: Mario Favaits
Загружено: 2021-10-22
Просмотров: 1064
This video provides a comprehensive guide to implementing anomaly detection using Python. It covers three main classifiers: Local Outlier Factor (LOF), One-Class SVM, and Isolation Forest, highlighting their applications, parameter settings, and performance metrics.
Libraries and Classifiers: Introduces the necessary Python libraries and the concept of anomaly detection. The video explains the usage of LOF, One-Class SVM, and Isolation Forest classifiers for identifying outliers in data.
Parameters Explained: Discusses the parameters for each classifier, such as the number of neighbors for LOF, the kernel type for One-Class SVM, and the number of trees for Isolation Forest.
Performance Comparison: Demonstrates the process of fitting these models on training data, making predictions, and evaluating their performance. Isolation Forest is highlighted for its superior accuracy and efficiency in anomaly detection.
Insights based on numbers:
LOF and One-Class SVM show lower performance scores (0.76 and lower), indicating limitations in handling outlier detection effectively.
Isolation Forest achieves a score above 0.95, showcasing its robustness and higher reliability in identifying anomalies.
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