Evaluation of classifiers
Evaluate Your Classifier Knowledge
Test your understanding of classifiers in machine learning with our engaging quiz! This quiz focuses on various ensemble classifiers, their parameters, and practical applications in accuracy assessment.
- 8 questions to challenge your knowledge
- Learn about Random Forest and more!
- Great for students, researchers, and anyone interested in machine learning
Which classifier is an ensemble classifier?
Random Forest
Support Vector Machines
Neural Network
Maximum Likelihood
Which of classifiers can handle well errors in training data?
Random Forest
Support Vector Machines
Neural Network
Maximum Likelihood
If you parameterize Random Forest, which parameters can you tune?
Number of trees
Bag fraction
Gamma
Features per split
Probability
Cost
What is a state of art of accuracy assessment?
Clear reports about sampling protocol
Estmitation of sample size
Account for spatial autocorrelation
Development of contingency table, calculation OA, UA, PA
Error-adjustment area estimation and building confidence intervals
Using training data for accuracy assessment
If you aim to boost classification accuracy, which steps would you follow?
Look at an increment of accuracy with and increase training data
Place more relevant satellite image dates to classify
Consider a fusion of multi-angle, multisource data
Revisit thematic classes an their separability
Get some coffee or tea, and revisit a code
Opt to another classifier
Work on tuning the parameters of classifier
Give up
When absolute atmospheric correction is needed?
We plan to build detailed time-series
We perform per band or index change detection
We use post-classification change detection
We use multilayerstack change detection
If you consider fusing various data and placing them into multilayerstack, what are your steps?
Absolute atmospheric correction
Precise coregistration
Resampling to the sample pixel size
If you consider fusing various data and placing them into time-series, what are your steps?
Absolute atmospheric correction
Radiometric rescaling
Precise coregistration
Additional correction for downgrading of the sensor over time
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