MS-DS Master of Data science Supervised Learning Algorithms Questions and Answers

Question 1

A data science team is developing a model to predict customer churn.
The lead data scientist is concerned about overfitting, as the initial Decision Tree model is achieving 99% accuracy on the training data but only 75% on the test data.
They also want a model that is robust to noise and provides feature importance rankings.
Which of the following algorithms would be the most appropriate next choice to address these specific concerns?

Accepted Answer

Random Forest

Answer

Support Vector Machine (SVM) with a linear kernel

Answer

K-Nearest Neighbors (KNN)

Answer

Logistic Regression

Question 2

You are tasked with building a classification model for a dataset that is not linearly separable in its original feature space. You want to use a powerful classification algorithm that can find a non-linear decision boundary. Which algorithm and concept combination is specifically designed for this purpose?

Accepted Answer

Support Vector Machine (SVM) with the Kernel Trick

Answer

Linear Regression with feature scaling

Answer

Logistic Regression with L2 regularization

Answer

Decision Tree with a maximum depth limit

Question 3

A team is building a predictive model and wants to use an ensemble method. They decide on an algorithm that builds trees sequentially, where each new tree is trained to correct the errors of the previous ones. This method is known for its high predictive accuracy but can be prone to overfitting if not carefully tuned. Which algorithm are they using?

Accepted Answer

Gradient Boosting

Answer

Random Forest

Answer

Bagging

Answer

AdaBoost

Question 4

In the context of regularized linear models, which of the following statements best describes the primary effect of L1 regularization (Lasso)?

Accepted Answer

It can shrink the coefficients of less important features to exactly zero, performing automatic feature selection.

Answer

It encourages all coefficient values to be small and non-zero, improving model stability.

Answer

It is primarily used to handle non-linear relationships by transforming features.

Answer

It has no effect on the model's coefficients but penalizes the intercept term to reduce bias.

Question 5

A data scientist is choosing between Random Forest and Gradient Boosting for a classification task on a large, noisy dataset. The project has a tight deadline, requiring a model that is relatively fast to train and less sensitive to hyperparameter tuning. Which algorithm is the better choice and why?

Accepted Answer

Random Forest, because its trees are built in parallel and it is generally less sensitive to hyperparameter changes.

Answer

Gradient Boosting, because its sequential nature allows for faster convergence on the optimal solution.

Answer

Gradient Boosting, because it can achieve higher accuracy, which is always the primary goal.

Answer

Random Forest, because it only uses a single decision tree, making it computationally simple.

Question 6

Which of the following supervised learning algorithms is inherently non-parametric and makes predictions for a new data point based on the majority class or average value of its 'k' closest neighbors in the feature space?

Accepted Answer

K-Nearest Neighbors (KNN)

Answer

Logistic Regression

Answer

Linear Discriminant Analysis (LDA)

Answer

Support Vector Machine (SVM)