Machine Learning Basics Quiz

What is the main difference between supervised and unsupervised learning?

• Number of iterations required

• Type of programming language used

• Use of artificial intelligence

• Presence of labeled data

Which type of learning is used when the output is continuous, such as predicting house prices?

• Classification

• Supervised learning

• Regression

• Clustering

What is the goal of classification in machine learning?

• To generate random numbers for the input data

• To sort the input data in ascending order

• To calculate the average of the input data

• Categorize or label input data into different classes or categories

What is the main objective of clustering in machine learning?

• To group similar data points together

• To organize data in alphabetical order

• To predict future data points

• To separate dissimilar data points

What is the process of finding a function that best fits a set of data points in regression?

• Guesswork

• Random selection

• Regression analysis

• Trial and error

What are the two main types of supervised learning algorithms?

• Past and Future

• Classification and Regression

• Addition and Subtraction

• Animals and Plants

What are the two main types of unsupervised learning algorithms?

• Supervised and reinforcement learning

• Regression and classification

• Decision tree and random forest

• Clustering and association

What is the difference between K-means and hierarchical clustering?

• K-means clustering is a partitioning method that requires the number of clusters to be specified in advance, while hierarchical clustering does not require the number of clusters to be known beforehand and produces a dendrogram to show the relationships between data points.

• K-means clustering uses a hierarchical approach, while hierarchical clustering uses a partitioning method

• K-means clustering does not require the number of clusters to be known beforehand, while hierarchical clustering requires the number of clusters to be specified in advance

• K-means clustering produces a dendrogram to show the relationships between data points, while hierarchical clustering requires the number of clusters to be specified in advance

What is overfitting in the context of machine learning?

• Overfitting is when a model learns the training data poorly and performs well on new data.

• Overfitting is when a model learns the training data just right and performs well on new data.

• Overfitting is when a model learns the training data too well and performs poorly on new data.

• Overfitting is when a model learns the training data perfectly and performs perfectly on new data.

What is the purpose of feature selection in machine learning?

• To decrease model performance

• To increase overfitting

• To improve model performance and reduce overfitting

• To make the model more complex