In QuickML, we can view the model metrics, which provide valuable insights into the performance of machine learning models. These metrics help assess the accuracy and effectiveness In QuickML, users have the capability to access and analyze the model metrics, which offer valuable insights into the performance of their machine learning models. These metrics serve as essential indicators to evaluate the accuracy and effectiveness of the models in making predictions on the provided dataset.

Some of the commonly observed model metrics accessible in QuickML are:

• Accuracy score: The proportion of correctly classified instances among the total instances.
• Precision score: The ratio of true positive predictions to the sum of true positive and false positive predictions.
• Recall score: The ratio of true positive predictions to the sum of true positive and false negative predictions.
• F1 score: The harmonic mean of precision and recall, providing a balanced assessment of the model’s performance.

By examining and interpreting these model metrics in QuickML, users can gain deeper insights into their machine learning models’ performance and make informed decisions on model selection and optimization. This empowers data scientists and developers to fine-tune their models and enhance predictive accuracy, leading to more effective and reliable predictions for various real-world applications.