Evaluating Classification and Regression Machine Learning Models
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Interested in what Machine Learning Metrics are applicable to Classification and Regression Models? Look no further! I go into depth on what metrics are most relevant and useful when working in the data science world. These metrics are a must know that set the foundation for all other machine learning models that you may use in the future.
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0:00 - Why do we care about Metrics?
1:40 - Confusion Matrix
2:20 - Sensitivity, Specificity, False Positive Rates
3:16 - Area Under the Curve (AUC-ROC)
4:15 - F1 Score
5:05 - Why using Regression metrics differ from those of Classification
6:48 - Mean Squared Error & Root Mean Squared Error
7:35 - Mean Absolute Error
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