Day 6 quiz

1. Which of the following is a regression problem?

• Predicting the price of a house based on its features.

• Classifying an email as spam or not spam.

• Identifying the breed of a dog from an image.

• Determining if a customer will churn or not.

Which of these are a part of Data Processing Pipeline?

• Data Cleaning

• Data representation

• Data Learning

• Data Mining

The goal of a classification model is to predict a continuous numerical value

• True
• False

In a decision tree, what does a leaf node represent?

• A decision rule based on a feature.

• The final predicted outcome or class label.

• The entire dataset.

• A random subset of features.

A model predicts whether a customer will click on an ad (Yes/No). What type of problem is this?

• Regression

• Clustering

• Classification

• Dimensionality Reduction

What is the purpose of 'bagging' in a Random Forest?

• To create deeper decision trees.

• To select the best features for splitting.

• To create diverse training datasets for each tree.

• To prune the decision trees after they are built.

Which of the following is a key difference between Linear Regression and Logistic Regression?

• Linear Regression is a supervised learning algorithm, while Logistic Regression is unsupervised.

• Linear Regression predicts continuous values, while Logistic Regression predicts discrete classes.

• Linear Regression is more complex than Logistic Regression.

• Linear Regression can only handle one feature, while Logistic Regression can handle many.

How does a Random Forest make a prediction in a classification task?

• It averages the predictions of all the trees.

• It uses the prediction of the deepest tree in the forest.

• It takes a majority vote from all the individual decision trees.

• It uses the prediction of the first tree that was built.

Which of these is best Decision Tree or Random Forest?

Which of the following is an example of an unsupervised learning problem?

• Predicting house prices based on historical data.

• Grouping customers into distinct segments based on their purchasing behavior.

• Identifying fraudulent credit card transactions from labeled data.

• Forecasting stock market prices.