Exploring Machine Learning Concepts

What is supervised learning?

• Supervised learning is a machine learning approach that uses labeled data to train models.

• Supervised learning is a type of reinforcement learning.

• Unsupervised learning uses labeled data to train models.

• Supervised learning is a method that requires no data for training.

Which of the following is an example of supervised learning?

• Clustering customers based on purchasing behavior.

• Segmenting images into different categories without labels.

• Predicting house prices based on features like size and location.

• Finding patterns in stock market data without prior knowledge.

In supervised learning, what is the role of the training data?

• The training data is only used for testing the model.

• The training data is used to validate the model's performance.

• The training data provides input-output pairs for the model to learn from.

• The training data generates random outputs for the model.

What is the main goal of unsupervised learning?

• To predict future outcomes based on past data.

• To enhance the accuracy of labeled datasets.

• To identify patterns or structures in data without labeled responses.

• To classify data into predefined categories.

Which of the following is a common algorithm used in unsupervised learning?

• Support vector machine

• Decision tree

• Linear regression

• K-means clustering

What is clustering in the context of unsupervised learning?

• Clustering is the process of grouping similar data points in unsupervised learning.

• Clustering is a method for supervised learning.

• Clustering involves labeling data points with predefined categories.

• Clustering is the process of sorting data points in ascending order.

What is a neural network?

• A neural network is a computational model that simulates the way human brains process information, consisting of interconnected layers of nodes.

• A neural network is a type of hardware used for gaming.

• A neural network is a biological system found in plants.

• A neural network is a social network for connecting people.

Which component of a neural network is responsible for learning?

• Optimizer

• Activation functions

• Weights and biases

• Loss function

What is the purpose of the activation function in a neural network?

• To normalize the input data.

• To increase the number of layers in the network.

• To reduce the training time of the model.

• The purpose of the activation function in a neural network is to introduce non-linearity.

What is overfitting in machine learning?

• Overfitting occurs when a model is too simple and cannot capture the underlying patterns.

• Overfitting is when a model performs well on training data but poorly on new data due to excessive complexity.

• Overfitting happens when a model is trained on too much data, leading to confusion.

• Overfitting is when a model performs poorly on both training and new data due to lack of data.