Gain insights into evaluating machine learning models with precision. Learn about the importance of labelling training data, the pitfalls of evaluating models with the same data used for training, and the diverse metrics beyond accuracy to measure a model’s performance. Enhance your understanding of machine learning evaluation techniques.
Table of Contents
Question
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Statement 1: Labelling is the process of tagging training data with known values.
Statement 2: You should evaluate a model by using the same data used to train the model.
Statement 3: Accuracy is always the primary metric used to measure a model’s performance.
Answer
Statement 1: Yes
Statement 2: No
Statement 3: No
Explanation
Statement 1: Yes. Labelling is the process of tagging training data with known values that can be used to train a supervised learning model.
Statement 2: No. You should evaluate a model by using a different data set than the one used to train the model. This is to avoid overfitting, which is when the model learns the specific patterns and noise in the training data and fails to generalize well to new data.
Statement 3: No. Accuracy is not always the primary metric used to measure a model’s performance. Accuracy is the ratio of correct predictions to total predictions, but it does not account for false positives and false negatives, which can be important in some scenarios. For example, in a medical diagnosis problem, you might want to minimize false negatives (missed diagnoses) even if it means increasing false positives (false alarms). Other metrics, such as precision, recall, and F1-score, can be used to measure a model’s performance based on different aspects of its predictions.
Reference
Microsoft Learn > Previous Versions > Azure > Train models > Evaluate model performance in Machine Learning Studio (classic)
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