FREE Data Science Supervised Learning Algorithms Questions and Answers

Question 1

A data scientist is building a model to classify emails as 'spam' or 'not spam'.
The dataset is large and has many features (words).
The scientist wants a model that is fast to train and makes a strong assumption that the presence of a particular word is unrelated to the presence of any other word.
Which algorithm is most suitable for this task?

Accepted Answer

Naive Bayes

Answer

Support Vector Machine (SVM)

Answer

K-Nearest Neighbors (KNN)

Answer

Decision Tree

Question 2

A financial institution wants to build a model to predict loan defaults. They have a dataset with many features, some of which are highly correlated. To prevent overfitting and improve model interpretability, they want to use a linear model that can also perform feature selection by shrinking some of the coefficients to exactly zero. Which of the following algorithms would be the best choice?

Accepted Answer

Lasso Regression (L1 Regularization)

Answer

Ridge Regression (L2 Regularization)

Answer

Linear Regression without regularization

Answer

K-Nearest Neighbors (KNN)

Question 3

When dealing with a complex, non-linearly separable dataset, which technique allows a Support Vector Machine (SVM) to find a separating hyperplane?

Accepted Answer

The Kernel Trick

Answer

Gradient Descent

Answer

Principal Component Analysis (PCA)

Answer

K-Means Clustering

Question 4

A developer is building a recommendation system for an e-commerce website. The goal is to recommend products to a user based on the products purchased by the 'K' most similar users. This is a classic application for which supervised learning algorithm?

Accepted Answer

K-Nearest Neighbors (KNN)

Answer

Logistic Regression

Answer

Random Forest

Answer

Support Vector Machine (SVM)

Question 5

Which of the following best describes the primary advantage of using a Random Forest algorithm over a single Decision Tree?

Accepted Answer

Reduced risk of overfitting and lower variance.

Answer

Higher interpretability and model simplicity.

Answer

Faster training time on large datasets.

Answer

Ability to handle non-linear data.

Question 6

In the context of ensemble learning, what is the key difference in how Bagging and Boosting train their base learners?

Accepted Answer

Bagging trains learners in parallel on different subsets of data, while Boosting trains them sequentially, with each learner focusing on the errors of the previous one.

Answer

Bagging trains learners sequentially, while Boosting trains them in parallel.

Answer

Bagging focuses on reducing bias, while Boosting focuses on reducing variance.

Answer

Bagging can only be used with Decision Trees, while Boosting can be used with any algorithm.