class_eval vs instance_eval

Class-level evaluation vs instance-level evaluation

Class-level evaluation and instance-level evaluation are two different approaches to evaluating the performance and effectiveness of machine learning models.

Class-level evaluation focuses on the overall performance of a machine learning model at the class level. It measures the model's ability to correctly classify all the samples in a class, as well as the overall performance of the model for all classes. Class-level evaluation is often used in scenarios where the model needs to achieve high accuracy in all classes, such as in image classification tasks.

Instance-level evaluation focuses on the performance of the model for each individual sample. It measures the model's ability to correctly classify each sample, as well as the performance of the model for each class. Instance-level evaluation is often used in scenarios where the model needs to achieve high accuracy for each individual sample, such as in text classification tasks.

Both class-level and instance-level evaluation are important for evaluating the performance of machine learning models. Class-level evaluation can provide a holistic view of the model's overall performance, while instance-level evaluation can provide a more detailed view of the model's performance for each individual sample.

To perform class-level evaluation, you can use tools such as accuracy, precision, recall, and F1-score. To perform instance-level evaluation, you can use tools such as confusion matrix, classification report, and accuracy.

Class-level evaluation

• Accuracy: Measures the proportion of correct predictions in each class.
• Precision: Measures the proportion of true positive predictions in each class.
• Recall: Measures the proportion of true positive predictions in all classes.
• F1-score: Measures the harmonic mean of precision and recall in all classes.

Instance-level evaluation

• Confusion matrix: Shows the performance of the model for each class and each instance.
• Classification report: Shows the performance of the model for each instance and each class.
• Accuracy: Measures the proportion of correct predictions for each instance.