ML Algorithms

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Q: What level is the ML Algorithms quiz?

This is a Medium-level quiz. Each question includes the correct answer so you learn as you test yourself, whatever your starting point.

Q: How can I prepare for this quiz?

Review the linked courses and notes for the topic first, then take the quiz to check your understanding. You can retake it anytime to track improvement.

Question 1

Linear regression minimizes which loss function to find the best fit line?

Accepted Answer

Linear regression minimizes Mean Squared Error (MSE) — the average of squared differences between predicted and actual values. This gives us the best-fit line that minimizes prediction errors.

Question 2

How does logistic regression produce probability outputs?

Accepted Answer

Logistic regression applies the sigmoid function σ(z) = 1/(1+e^-z) to the linear combination of features, squashing the output to a probability between 0 and 1. Values above 0.5 are classified as positive.

Question 3

What criteria does a decision tree use to decide how to split the data at each node?

Accepted Answer

Decision trees split data by choosing the feature and threshold that best reduces impurity. Gini impurity measures how often a randomly chosen element would be incorrectly labeled; entropy measures information disorder. Both guide splits to create purer child nodes.

Question 4

What is a Random Forest?

Accepted Answer

Random Forest is an ensemble method that builds many decision trees, each trained on a random bootstrap sample of data and a random subset of features. The final prediction aggregates (votes or averages) all trees, reducing overfitting and improving accuracy.

Question 5

How does K-Nearest Neighbors (KNN) make predictions?

Accepted Answer

KNN is a lazy learning algorithm — it stores all training data and at prediction time finds the k nearest neighbors by distance (usually Euclidean). Classification takes a majority vote; regression averages the neighbors' values.

Question 6

K-means is an unsupervised learning algorithm that groups data into k clusters without needing labeled data.

Accepted Answer

True. K-means clustering is unsupervised — it partitions data into k clusters by iteratively assigning points to the nearest centroid and updating centroids until convergence. No labels are needed; it discovers natural groupings.

Question 7

What does a Support Vector Machine (SVM) try to find?

Accepted Answer

SVM finds the decision boundary (hyperplane) that maximizes the margin — the distance between the boundary and the nearest data points of each class (called support vectors). Larger margins generally lead to better generalization.

Question 8

What is gradient descent used for in machine learning?

Accepted Answer

Gradient descent is an optimization algorithm that iteratively adjusts model parameters in the direction opposite to the gradient of the loss function. Each step reduces the loss, eventually converging to a minimum. Variants include SGD, Mini-batch GD, Adam, and RMSProp.

Question 9

What is L1 regularization (Lasso) primarily used for?

Accepted Answer

L1 regularization (Lasso) adds the absolute value of coefficients as a penalty to the loss. This drives unimportant feature coefficients to exactly zero, effectively performing automatic feature selection. L2 (Ridge) shrinks coefficients but rarely to zero.

Question 10

How is precision calculated in a binary classification task?

Accepted Answer

Precision = TP / (TP + FP). It measures how many of the model's positive predictions were actually correct. High precision means few false positives. Contrast with recall = TP / (TP + FN) which measures how many actual positives were found.

ML Algorithms

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ML Algorithms

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Is the ML Algorithms quiz free?

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ML Fundamentals

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