CT1- ML

What is true about Machine Learning?

• A. Machine Learning (ML) is that field of computer science
• B. ML is a type of artificial intelligence that extract patterns out of raw data by using an algorithm or method.
• C. The main focus of ML is to allow computer systems learn from experience without being explicitly programmed or human intervention.
• D. All of the above

Which of the following is an example of a classification problem?

• a) Predicting the price of a house based on its features
• b) Predicting the weight of a person based on their height
• c) Predicting whether a customer will churn or not
• d) Predicting the age of a person based on their income

Which of the following is an example of a clustering algorithm?

• a) Decision tree
• b) Random forest
• c) K-means
• d) Gradient descent

What is the purpose of cross-validation in machine learning?

• a) To evaluate the performance of a model on a held-out test set
• b) To evaluate the performance of a model on different subsets of the data
• c) To compare the performance of different models
• d) To tune the hyperparameters of a model

Predicting whether a tumour is malignant or benign is an example of?

• (A) Unsupervised Learning
• (B) Supervised Regression Problem
• (C) Supervised Classification Problem
• (D) Categorical Attribute

When the number of features increase

• (A) Computation time increases
• (B) Model becomes complex
• (C) Learning accuracy decreases
• (D) All of the above

In what type of learning labelled training data is used

• A. unsupervised learning
• B. supervised learning
• C. reinforcement learning
• D. active learning

Data used to build a data mining model.

• A. training data
• B. validation data
• C. test data
• D. hidden dataF

The problem of finding hidden structure in unlabeled data is called…he problem of finding hidden structure in unlabeled data is called…

• A. supervised learning
• B. unsupervised learning
• C. reinforcement learning
• D. none of the above

Decision tree is a ______ algorithm.

• (A) supervised learning
• (B) unsupervised learning
• (C) Both
• (D) None of these