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  • Evaluating_Classification_Models_Performance

Course Content

FAQs

The performance of a classification model where the prediction is a probability value between 0 and 1 is measured by logarithmic loss (or log loss). As the anticipated probability diverges from the actual label, log loss grows. For Kaggle contests, log loss is a popular measure.

The evaluation of performance is an important part of the machine learning process. It is, however, a difficult task. As a result, it must be carried out with caution if machine learning can be applied to radiation oncology or other fields with confidence.

Model assessment is the process of analysing a machine learning model's performance, as well as its strengths and weaknesses, using various evaluation criteria. Model evaluation is critical for determining a model's efficacy during the early stages of research, as well as for model monitoring.

The main types of evaluation are processes:

  • impact
  • outcome
  • summative
  • evaluation.

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