Understand model evaluation: from labelling training data to metric considerations. Explore beyond accuracy as the sole performance measure in machine learning.
HOTSPOT -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.
Statement 1: Labelling is the process of tagging training data with known values: Yes
Statement 2: You should evaluate a model by using the same data used to train the model: No
Statement 3: Accuracy is always the primary metric used to measure a model’s performance: No
Box 1: Yes -In machine learning, if you have labeled data, that means your data is marked up, or annotated, to show the target, which is the answer you want your machine learning model to predict. In general, data labeling can refer to tasks that include data tagging, annotation, classification, moderation, transcription, or processing.
Box 2: No –
Box 3: No -Accuracy is simply the proportion of correctly classified instances. It is usually the first metric you look at when evaluating a classifier. However, when the test data is unbalanced (where most of the instances belong to one of the classes), or you are more interested in the performance on either one of the classes, accuracy doesn’t really capture the effectiveness of a classifier.
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