ML Zoomcamp Week 4
This is week 4 of #mlzoomcamp and it was all about ML Evaluation
This week covered evaluation Metrics on classification models.
After training a model, it’s perfor,mance needs to be evaluated on a test set. This helps to understand how well the model will generalize on a new data.
There are a number of different evaluation metrics that we can use for binary classification problems. Some of the most common metrics include:
Accuracy
Precision
Recall
F1 Score
ROC Curve and AUC
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Week 4 of ML Zoomcamp focuses on evaluating the performance of machine learning models using various metrics for binary classification problems. Key metrics discussed include accuracy, precision, recall, F1 score, ROC Curve, and AUC. Evaluating these metrics on a test set helps in understanding how well a model will generalize to new data.