Machine Learning test (Intermediate)

Sample questions

5 sample questions from the Machine Learning test, with the answers. Samples are taken out of the live test, so you will not meet these when you sit it.

1. Which of the following correctly defines precision in a classification model?

   1. Accuracy = (TP + FN)/all
   2. Precision = TP/(TP + FP)
   3. Recall = FP/(TP + FP)
   4. F1 = (Precision + Recall)/2

   Answer: B. Precision = TP/(TP + FP)

   Precision is defined as the ratio of true positives to the sum of true positives and false positives. This metric indicates how many of the predicted positive instances are actually positive. Accuracy is (TP + TN)/all, recall is TP/(TP + FN), and F1 is the harmonic mean of precision and recall.

2. If both the training and validation errors are high and close to each other, what is this an indication of?

   1. Low bias and high variance
   2. High variance
   3. Low bias and low variance
   4. High bias

   Answer: D. High bias

   When both the training and validation errors are high and close to each other, it indicates that the model is underfitting, which is associated with high bias. This occurs when the model is too simple to capture the underlying patterns in the data. A large gap between training and validation errors, on the other hand, indicates high variance (overfitting).

3. Which of the following correctly describes the behavior of the first principal component in principal component analysis (PCA)?

   1. The first principal component captures the direction of average variance in the data
   2. The first principal component captures the direction of maximum variance in the data
   3. The first principal component captures the direction of zero variance in the data
   4. The first principal component captures the direction of minimum variance in the data

   Answer: B. The first principal component captures the direction of maximum variance in the data

   Principal component analysis (PCA) identifies directions (principal components) that maximize variance in the data. The first principal component corresponds to the direction of highest variance, and subsequent components are orthogonal to the first and capture the next highest variance, and so on. This ensures that the first component accounts for the largest possible amount of variability in the dataset.

4. Which metric measures the proportion of true positive predictions to the total number of positive predictions made by a model?

   1. Accuracy
   2. F1 score
   3. Precision
   4. Recall

   Answer: C. Precision

   Precision is the ratio of true positive predictions to the total number of positive predictions made by the model. It measures how many of the positively predicted instances are actually correct. This is particularly important in scenarios where false positives are costly, such as in medical diagnoses or spam detection.

5. Which of the following statements correctly describes the F1 score?

   1. The F1 score is the harmonic mean of precision and recall.
   2. The F1 score is calculated as the sum of precision and recall.
   3. The F1 score is equal to the product of precision and recall.
   4. The F1 score is calculated as the arithmetic mean of precision and recall.

   Answer: A. The F1 score is the harmonic mean of precision and recall.

   The F1 score is calculated as the harmonic mean of precision and recall, making it a balanced measure of model performance. Other options are incorrect because they either describe unrelated metrics or misrepresent the F1 score calculation.