Understanding Machine Learning Concepts

What is machine learning?

• Training computers to make decisions based on data

• Automating simple tasks without data

• Using hardware to increase computational speed

• Programming rules manually into a computer

Which of the following is a key characteristic of supervised learning?

• It only focuses on clustering techniques

• It works with unstructured data

• It uses labeled data for training

• It does not require any data

What is the primary purpose of a training dataset?

• To train the model by learning patterns in the data

• To enhance data visualization

• To store data securely

• To provide data for testing

Which type of machine learning is most suitable for customer segmentation?

• Semi-supervised learning

• Deep learning

• Transfer learning

• Unsupervised learning

Which of the following is an example of a feature in a dataset about houses?

• The color of the house

• The number of windows

• The type of roof

• A

What is the main difference between supervised and unsupervised learning?

• There is no difference

• Unsupervised learning is faster

• Supervised learning is only for classification tasks

• Supervised learning uses labeled data, unsupervised does not

Which algorithm is commonly used for classification tasks?

• Linear Regression

• K-means

• Decision Trees

• None of the above

What does overfitting mean in machine learning?

• The model generalizes well to new data

• The model is trained on too little data

• The model has too few parameters

• A. The model performs well on training data but poorly on unseen data

What is a common evaluation metric for regression models?

• Log Loss

• Area Under Curve

• Mean Squared Error

• R-squared

Which of the following best describes reinforcement learning?

• Learning through trial and error

• Learning from historical trends

• Learning through observation

• Learning from supervised feedback