Machine Learning

What is supervised learning?

• Supervised learning is only used for regression tasks

• Supervised learning does not require labeled data

• Supervised learning is a type of unsupervised learning

• Supervised learning is a type of machine learning where the model is trained on labeled data.

Give an example of an unsupervised learning algorithm.

• Decision Tree

• K-Means Clustering

• Logistic Regression

• Linear Regression

Explain the concept of unsupervised learning.

• Unsupervised learning does not involve finding patterns in data.

• Unsupervised learning is only used for classification tasks.

• Unsupervised learning aims to discover patterns and relationships within the data.

• Unsupervised learning requires labeled data for training.

Name a popular supervised learning model to solve a classification problem.

• Logistic regression

• K-means clustering

• Linear regression

• Principal component analysis

What is reinforcement learning?

• Reinforcement learning uses an agent who learns to make decisions by receiving feedback.

• Reinforcement learning involves only unsupervised learning techniques

• Reinforcement learning is a type of supervised learning

• Reinforcement learning does not involve feedback mechanisms

Provide a real-world application of reinforcement learning.

• Training chatbots

• Predicting stock prices

• Playing chess

• Autonomous driving

What is the main difference between supervised and unsupervised learning?

• Supervised learning involves training a model on labeled data, while unsupervised learning involves finding patterns in unlabeled data.

• Supervised learning involves finding patterns in labeled data, while unsupervised learning involves training a model on unlabeled data.

• Supervised learning requires human intervention, while unsupervised learning is fully automated.

• Supervised learning is used for classification tasks, while unsupervised learning is used for regression tasks.

How does a supervised learning model make predictions?

• By learning from a labeled dataset during training and using the learned patterns to predict output labels for new data.

• By predicting based on the most common label in the dataset

• By using unsupervised learning techniques

• By guessing randomly without any training

What is clustering in unsupervised learning?

• Clustering aims to assign predefined labels to data points

• Clustering does not involve grouping similar data points together

• Clustering aims to find natural groupings in data.

How does reinforcement learning agent learn to make decisions?

• Reinforcement learning agents learn to make decisions based on random guesses.

• Reinforcement learning agents learn to make decisions through trial and error

• Reinforcement learning agents learn to make decisions by mimicking human behavior.

• Reinforcement learning agents learn to make decisions by following pre-defined rules.