Binary Classification Quiz

What is the main difference between regression and classification in machine learning?

• Regression models are unsupervised, while classification models are supervised

• Regression models use logistic regression, while classification models use linear regression

• Regression models calculate numeric values, while classification models calculate probability values for class assignment

• Regression models predict true or false, while classification models predict continuous values

What is the purpose of binary classification algorithms?

• To predict true or false for multiple classes

• To predict one of two possible labels for a single class

• To predict continuous values for a single class

• To predict multiple labels for a single class

In the example provided, what feature is used to predict whether the patient has diabetes?

• Heart rate

• Blood pressure

• Cholesterol level

• Blood glucose level

What is the range of probability values calculated by binary classification algorithms?

• 0.0 to 1.0

• 0.0 to 10.0

• 1.0 to 100.0

• 0.0 to 100.0

Which algorithm is commonly used for binary classification?

• Logistic regression

• Linear regression

• Random forest

• Decision tree

What is the formula for calculating accuracy in binary classification?

• (2 x Precision x Recall) ÷ (Precision + Recall)

• (TP) ÷ (TP+FP)

• (TP) ÷ (TP+FN)

• (TN+TP) ÷ (TN+FN+FP+TP)

What does the recall metric measure in binary classification?

• The proportion of positive cases that the model identified correctly

• The proportion of predicted positive cases where the true label is actually positive

• The overall metric that combines recall and precision

• The area under the curve (AUC)

What is the precision metric in binary classification?

• The proportion of predicted positive cases where the true label is actually positive

• The proportion of positive cases that the model identified correctly

• The overall metric that combines recall and precision

• The area under the curve (AUC)

What is the formula for calculating F1-score in binary classification?

• (TP) ÷ (TP+FN)

• (TP) ÷ (TP+FP)

• (2 x Precision x Recall) ÷ (Precision + Recall)

• (TN+TP) ÷ (TN+FN+FP+TP)

What does the Area Under the Curve (AUC) metric measure in binary classification?

• The proportion of predicted positive cases where the true label is actually positive

• The performance of the model at predicting whether or not a patient has diabetes

• The proportion of positive cases that the model identified correctly

• The overall metric that combines recall and precision