📌 Model Performance Metrics Summary
Model performance metrics are measurements that help us understand how well a machine learning model is working. They show if the model is making correct predictions or mistakes. Different metrics are used depending on the type of problem, such as predicting numbers or categories. These metrics help data scientists compare models and choose the best one for a specific task.
🙋🏻♂️ Explain Model Performance Metrics Simply
Think of model performance metrics like a scorecard for a sports team. After each game, you look at the score to see how well the team played. In the same way, these metrics give a score to a model, showing if it is making good or bad predictions. This helps you decide if the model is ready to be used or if it needs more practice.
📅 How Can it be used?
Model performance metrics can track how accurately a spam filter sorts emails in a mail application.
🗺️ Real World Examples
A hospital uses a machine learning model to predict if patients are at risk of developing diabetes. By checking metrics like accuracy and recall, doctors can see if the model is correctly identifying patients who need extra care.
An online shop uses a model to recommend products to customers. By measuring precision and click-through rate, the team can see if the recommendations are relevant and improve customer engagement.
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