Introduction to Machine Learning

What is the main difference between supervised and unsupervised learning?

• Unsupervised learning always produces more accurate results than supervised learning.

• Supervised learning requires more computational power than unsupervised learning.

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

• Supervised learning requires labeled data, while unsupervised learning works on unlabeled data.

Give an example of a classification problem in machine learning.

• Detecting anomalies in network traffic

• Classifying emails as spam or not spam

• Predicting stock prices

• Identifying images as cats or dogs

Explain the concept of regression in machine learning.

• Regression predicts discrete values based on input features.

• Regression in machine learning is a supervised learning technique used to predict continuous values based on input features.

• Regression is an unsupervised learning technique used for classification tasks.

• Regression is only applicable to text data.

What is clustering and how is it different from classification?

• Clustering predicts the class of an object based on its features, while classification groups objects without predefined classes.

• Clustering is used for supervised learning, while classification is used for unsupervised learning.

• Clustering is grouping objects based on similarity without predefined classes, while classification predicts the class of an object based on its features.

• Clustering is assigning objects to predefined classes, while classification groups objects based on similarity.

In supervised learning, what is the role of the target variable?

• The target variable is used for model evaluation

• The target variable is the input variable

• The target variable is irrelevant in supervised learning

• The role of the target variable is to be the output or dependent variable that the model aims to predict.

Name a popular algorithm used for classification tasks.

• Support Vector Machine

• K-Means

• Random Forest

• Decision Tree

How is K-means clustering algorithm different from hierarchical clustering?

• K-means clustering and hierarchical clustering both assign data points to clusters based on the nearest centroid.

• K-means clustering builds a tree of clusters by merging or splitting them based on similarity, while hierarchical clustering assigns data points to clusters based on the nearest centroid.

• K-means clustering assigns data points to clusters based on the nearest centroid, while hierarchical clustering builds a tree of clusters by merging or splitting them based on similarity.

• K-means clustering and hierarchical clustering both build a tree of clusters by merging or splitting them based on similarity.

What is the purpose of feature selection in machine learning?

• Feature selection is only necessary for small datasets

• Feature selection is used to increase the complexity of the model

• The purpose of feature selection in machine learning is to choose the most relevant features that contribute the most to the prediction task.

• Feature selection has no impact on the model's performance

Describe the process of training a machine learning model.

• Selecting the most expensive algorithm

• The process of training a machine learning model involves selecting an algorithm, preparing the data, feeding the training data, adjusting parameters, evaluating performance, and fine-tuning.

• Skipping the evaluation step

• Feeding the testing data instead of training data

What evaluation metrics are commonly used for regression models?

• Mean Absolute Error

• Mean Squared Logarithmic Error

• Mean Absolute Percentage Error

• MSE, RMSE, MAE, R-squared, Adjusted R-squared