Machine Learning Quiz

What does the K-Nearest Neighbors (KNN) graph represent?

• A line that best fits the data

• Decision boundaries created based on the closest data points

• A neural network with multiple layers

• A tree structure used for splitting data

Which of the following is an example of regression?

• Predicting the species of a flower based on petal length

• Identifying spam emails

• Estimating the price of a house based on square footage

• Classifying cats and dogs in images

Which of the following is true for classification?

• It predicts continuous values.

• It assigns categories or labels to data points.

• It always requires labeled data.

• It cannot use decision trees.

What is the primary difference between supervised and unsupervised learning?

• Supervised learning uses labeled data, while unsupervised learning does not.

• Supervised learning always uses neural networks, while unsupervised learning does not.

• Supervised learning cannot handle regression tasks.

• Unsupervised learning is faster than supervised learning.

Which of the following is an example of unsupervised learning?

• Predicting stock prices

• Clustering customers based on purchasing habits

• Classifying email as spam or not spam

• Forecasting weather

What does the train-test split help with (Choose 2)?

• Reducing the amount of data needed for a model

• Ensuring the model generalizes well to unseen data

• Making the training process faster

• Detect overfitting

Which of the following best defines overfitting?

• The model performs poorly on both training and test data.

• The model performs well on training data but poorly on test data.

• The model cannot learn patterns in the training data.

• The model is too simple for the problem at hand.

What is underfitting?

• When the model is too complex and memorizes the data

• When the model cannot capture the patterns in the data

• When the model performs better on the test set than on the training set

• When the model uses too much computational power

What does generalization mean in machine learning?

• A model's ability to perform well on unseen data

• A model's accuracy on the training data

• Scaling the features to have values between -1 and 1

• Adding polynomial features to the model

What is the bias-variance tradeoff?

• Balancing the model's performance on training and test data to minimize total error

• Ensuring the model always predicts the mean value of the target variable

• Using a large dataset to reduce variance

• Avoiding the use of complex models to reduce bias