Model Evaluation Metrics. F1score, ROC curve and AUC, Confusion Matrix.

#omega_teched #model_evaluation_metrics #data_science

Machine Learning :
Supervised learning: Classification and Regression
https://www.youtube.com/watch?v=c4YOLFrhAgQ&list=PLG-cz6t92P1EfPTlui349x3R0DdyZ-xB4&index=8&t=13s

Unsupervised Learning: Clustering
https://www.youtube.com/watch?v=7MRLDU92hPU&list=PLG-cz6t92P1EfPTlui349x3R0DdyZ-xB4&index=11&t=315s

Machine Learning Algorithms:
K-Nearest Neighbors (KNN) Decision Tree, Bayes Theorem
https://www.youtube.com/watch?v=aJw0i2wDoUo&list=PLG-cz6t92P1EfPTlui349x3R0DdyZ-xB4&index=9

Regression, Artificial Neural Network (ANN), Support Vector Machines (SVM)
https://www.youtube.com/watch?v=SgdYV4uKL4A&list=PLG-cz6t92P1EfPTlui349x3R0DdyZ-xB4&index=10

Ensemble Learning: Boosting and Bagging
https://www.youtube.com/watch?v=Zgpa4-mO3a0&list=PLG-cz6t92P1H3OJJF1pfRRGVFwzSbut8A&index=3

In this video we will cover the concept of Evaluation Metrics: Accuracy, Precision and Recall.
00:00 Introduction
00:18 F1 score
03:30 ROC curve and AUC
10:35 Confusion Matrix
13:45 Evaluating model for imbalanced dataset.

Тэги:

#data_science #tycs_sem_6 #model_evaluation_metrics #f1_score #roc_curve #auc #confusion_matrix #how_to_evaluate_imbalanced_dataset

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