Decision Trees in Data Classification

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What is one of the advantages of using decision trees in machine learning?

Limited interpretability and visualization

Prone to underfitting with complex datasets

Not suitable for large-sized datasets

Handles non-linear relationships well (correct)

Which technique is crucial for assessing the accuracy and generalizability of machine learning models?

Model validation (correct)

Model exploration

Overfitting prevention

Data preprocessing

What is a common limitation associated with decision trees in machine learning?

Inability to handle non-linear relationships

Tendency to overfit, especially with complex datasets (correct)

Excellent performance with small-sized datasets

Resistance to overfitting

What is a key advantage of decision trees in image classification?

Allows for more control over the classification process Signup and view all the answers

What is a disadvantage of decision trees in image classification as mentioned in the text?

Prone to overfitting if training samples are not representative Signup and view all the answers

How does unsupervised image classification differ from decision trees?

It groups pixels into clusters based on spectral values without prior information Signup and view all the answers

Which clustering algorithm is known for its flexibility in allowing cluster merging and splitting?

ISODATA Signup and view all the answers

What is a disadvantage of hierarchical clustering mentioned in the text?

Determining appropriate number of clusters Signup and view all the answers

Which clustering algorithm does not need the collection of training samples?

Hierarchical clustering Signup and view all the answers

What is a key challenge mentioned for users of unsupervised classification algorithms?

Assigning accurate labels Signup and view all the answers

What is the key difference between supervised and unsupervised classification methods in GIS?

Supervised classification requires a set of known, labeled samples for training, while unsupervised classification does not. Signup and view all the answers

Which step is NOT part of the process for supervised classification in GIS?

Object-based image analysis Signup and view all the answers

What is the primary assumption of the maximum likelihood classifier used in supervised classification?

Each class follows a Gaussian distribution Signup and view all the answers

What is the main objective of support vector machines in supervised classification?

To find the optimal hyperplane to separate different classes by maximizing the margin between them Signup and view all the answers

What is the main difference between supervised and unsupervised classification?

Supervised classification requires labeled training data, while unsupervised classification does not. Signup and view all the answers

Which of the following accurately describes the user involvement in supervised and unsupervised classification?

Supervised classification requires users to select training samples; unsupervised classification needs minimal user intervention. Signup and view all the answers

What is a key advantage of supervised classification over unsupervised classification?

Supervised classification may have higher accuracy due to the use of training data. Signup and view all the answers

Which statement best describes the difference in complexity between supervised and unsupervised classification?

Unsupervised classification is generally more complex due to the training process involved. Signup and view all the answers