What is the significance of model evaluation in machine learning?

Model evaluation ensures system reliability.

• Assesses model performance metrics.

• Identifies strengths and weaknesses.

• Facilitates model improvement cycles.

Fill in the blank: The process of splitting the dataset into training and testing sets is known as ___ split.

Train-test

Explain how to calculate the accuracy of a machine learning model.

Calculate accuracy of ML model.

• Identify correct predictions.

• Use accuracy formula.

• Calculate TP, TN, FP, FN.

True or False: A confusion matrix only provides information about the true positive and true negative values of a model.

False. A confusion matrix also includes false positive and false negative values, providing a complete picture of the model's performance.

What does the F1 score represent in the context of model evaluation?

F1 Score Represents Precision and Recall

• Combines two important metrics.

• Useful for imbalanced datasets.

• Single score for model evaluation.

Riddle: I help you choose the best prediction model, but I’m not a fortune teller. What am I?

An evaluation metric.

Describe the ethical concerns associated with model evaluation.

Model evaluation raises ethical concerns.

• Bias can lead to unfair predictions.

• Transparency builds trust in AI.

• Accuracy is vital for reliability.

What is the role of accuracy and error in evaluating AI models?

Accuracy and error evaluate AI models effectively.

• Accuracy shows correct predictions.

• Error highlights prediction flaws.

• Together, they guide improvements.

Fill in the blank: The metric that considers both true positives and false positives to measure the correctness of positive predictions is known as ___ .

Precision

How can we interpret a situation where a model has high accuracy but performs poorly in real-world applications?

High accuracy can be misleading.

• Unbalanced datasets skew results.

• Consider precision and recall.

• F1 score offers balanced view.