Machine Learning Basics Quiz

What is supervised learning in machine learning?

• A type of learning where the model learns from data that is not labeled.

• A type of learning where the model learns from labeled data to predict outcomes.

• A method where the model organizes the data into clusters without prior training.

• The process of improving model performance by increasing the complexity of the model.

Answer explanation

Supervised learning is when the model learns from labeled data to predict outcomes.

Which of the following is an example of unsupervised learning?

• Classification

• Regression

• Clustering

• Decision Trees

Answer explanation

Clustering is an example of unsupervised learning. It involves grouping similar data points together without any predefined labels or target variables.

What is the main goal of a neural network?

• To organize data into meaningful groups without prior labels.

• To explicitly program the computer to perform specific tasks.

• To mimic the human brain and learn from vast amounts of data.

• To reduce the dimensionality of the input data.

Answer explanation

The main goal of a neural network is to mimic the human brain and learn from vast amounts of data.

In supervised learning, what is the difference between classification and regression?

• Classification deals with predicting a continuous value, while regression deals with predicting a discrete label.

• Classification and regression are the same thing.

• Classification deals with predicting a discrete label, while regression deals with predicting a continuous value.

• There is no difference; both are types of unsupervised learning.

Answer explanation

Classification predicts discrete labels, while regression predicts continuous values.

Which of the following is true about neural networks?

• They can only be used for supervised learning tasks.

• They are primarily used for clustering large datasets.

• They consist of layers of nodes, including input, hidden, and output layers.

• They do not require a large amount of data to perform well.

Answer explanation

Neural networks consist of input, hidden, and output layers, making them the correct choice.