Machine Learning Machine Learning 2

Question 1

Which regularization technique adds the sum of the absolute values of coefficients to the loss function?

Accepted Answer

Lasso (L1)

Answer

Lasso (L1) regularization adds the sum of absolute coefficient values, which can shrink some coefficients to exactly zero, performing feature selection.

Question 2

In a confusion matrix, what does a False Negative represent?

Accepted Answer

Model predicted negative but the true label was positive

Answer

A False Negative occurs when the model predicts the negative class but the true label is actually positive, meaning a positive case was missed.

Question 3

What is the purpose of the kernel trick in Support Vector Machines?

Accepted Answer

To map data into a higher-dimensional space without explicit computation

Answer

The kernel trick allows SVMs to operate in high-dimensional feature spaces by computing dot products implicitly, enabling non-linear classification without explicit transformation.

Question 4

Which ensemble method trains models sequentially, each correcting errors of the previous one?

Accepted Answer

Boosting

Answer

Boosting trains weak learners sequentially, where each new model focuses on the errors made by the previous ensemble, reducing bias.

Question 5

What does the term 'epoch' mean in the context of neural network training?

Accepted Answer

One complete pass through the entire training dataset

Answer

An epoch refers to one complete pass of the entire training dataset through the neural network during the training process.

Machine Learning Practice Test

Machine Learning Practice Test

Machine Learning Machine Learning 2