1. Which type of Machine Learning involves training on labeled data?
A) Unsupervised Learning
B) Reinforcement Learning
C) Supervised Learning
D) Semi-Supervised Learning
2. What is the main goal of Unsupervised Learning?
A) Predict a continuous value
B) Classify data into predefined categories
C) Identify hidden patterns or groupings in unlabeled data
D) Train a model using labeled examples
3. Which algorithm is used for binary classification problems?
A) Linear Regression
B) Decision Tree
C) Logistic Regression
D) K-Means Clustering
4. What is the purpose of a Decision Tree algorithm?
A) To predict a continuous outcome based on linear relationships
B) To classify data or make regression predictions by creating a tree-like model
C) To find clusters in unlabeled data
D) To optimize decisions in real-time scenarios
5. Why is data preprocessing essential in Machine Learning?
A) To automatically select the best algorithm for a problem
B) To clean and transform raw data into a suitable format for modeling
C) To increase the number of features in the dataset
D) To reduce the size of the dataset
6. What does feature engineering involve?
A) Selecting and modifying features to enhance model performance
B) Building neural networks with many layers
C) Training models using labeled data
D) Clustering data points into groups
7. Which of the following is a key difference between Supervised and Unsupervised Learning?
A) Supervised Learning uses labeled data, while Unsupervised Learning uses unlabeled data
B) Unsupervised Learning is only used for regression tasks
C) Supervised Learning does not require feature engineering
D) Unsupervised Learning cannot handle continuous data
8. What type of learning is Reinforcement Learning best suited for?
A) Classifying data into categories
B) Predicting continuous values
C) Making decisions through trial and error based on feedback
D) Finding clusters in data
9. Which algorithm would you use to predict a house price based on multiple features such as size and location?
A) Logistic Regression
B) Decision Tree
C) Linear Regression
D) K-Means Clustering
10. What is the primary objective of feature engineering?
A) To reduce the complexity of Machine Learning models
B) To create new features or modify existing ones to improve model performance
C) To automate the data cleaning process
D) To convert labeled data into unlabeled data