Machine Learning Flashcards

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

Answer: Lasso (L1)

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

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

Answer: Model predicted negative but the true label was positive

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

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

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

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

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

Answer: Boosting

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

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

Answer: One complete pass through the entire training dataset

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

Which dimensionality reduction technique is unsupervised and maximizes variance in the projected space?

Answer: Principal Component Analysis (PCA)

PCA is an unsupervised technique that finds orthogonal components that maximize the variance in the data, projecting it onto a lower-dimensional space.

In k-means clustering, what is the objective function being minimized?

Answer: Within-cluster sum of squared distances to centroids

K-means minimizes the within-cluster sum of squared distances (inertia), assigning each point to the nearest centroid and updating centroids iteratively.