Which indicators are used for model evaluation in classification? (Select all that apply)

Question

BlackTom AI · Accepted Answer

To begin, consider the task: identifying indicators used for evaluating classification models.
Option 1: PCA. This stands for Principal Component Analysis, which is a dimensionality reduction technique, not an evaluation metric. While PCA can be used in preprocessing or visualization, it does not measure classification performance, so this option is not appropriate as an evaluation indicator.
Option 2: Precision. Precision is defined as the ratio of true positives to the sum of true positives and false positives. It directly reflects how trustworthy the positive predictions are in a classification task, making it a valid evaluation metric for classification models.
Option 3: Recall. Recall, also known as sensitivity or true positive rate, is the ratio of true positives to the sum of true positives and false negatives. It measures how many actual positive instances the model correctly identifies, and it is a standard metric for classification performance.
Option 4: R-squared. R-squared is a statistic used to assess goodness-of-fit for regression models, indicating how well the variance of the outcome is explained by the model. It is not applicable as an evaluation metric for classification tasks.
In summary, among the given options, Precision and Recall are the appropriate indicators for evaluating classification models, while PCA and R-squared do not serve as standard classification evaluation metrics.

Which indicators are used for model evaluation in classification? (Select all that apply)

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