Data Science Quiz

Question 1

What is a confusion matrix?

Accepted Answer

A confusion matrix lets you see for a given model how your predictions compare with the actual results. It’s a 2x2 grid that has four parts: the number of true positives, false positives, true negatives, and false negatives.

Question 2

What two parameters defined a normal distribution?

Accepted Answer

Its mean and its standard deviation.

Question 3

Which regularization would you use if your model is underfitting?

Accepted Answer

None, because regularization is used in case of overfitting.

Question 4

What is a false positive?

Accepted Answer

An example in which the model mistakenly predicted the positive class.

Question 5

What is early stopping?

Accepted Answer

A method for regularization that involves ending model training before training loss finishes decreasing. In early stopping, you end model training when the loss on a validation dataset starts to increase.

Question 6

What is the difference between a dense and a sparse feature?

Accepted Answer

A dense feature is one in which most values are non-zero in contrast with a sparse one where most values are zeros or empty.

Question 7

Name three unsupervised learning algorithms.

Accepted Answer

Clustering, Anomaly Detection, Latent variable models, Autoencoders, etc.

Question 8

Is rotation necessary in PCA?

Accepted Answer

Yes, rotation is necessary because it maximizes the difference between variance captured by the component.

Question 9

Explain what is Bias-Variance tradeoff?

Accepted Answer

Expected error splits into bias (a model too simple for the truth), variance (sensitivity to which training sample you drew) and irreducible noise. Pushing one down usually lifts the other, so you tune capacity and regularization to minimise the sum, not either term alone.

Question 10

What is the precision-recall curve?

Accepted Answer

A precision-recall curve (or PR Curve) is a plot of the precision (y-axis) and the recall (x-axis) for different probability thresholds.

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