Evaluation of classifiers

An educational and engaging image depicting different machine learning classifiers, such as Random Forest, Support Vector Machines, and Neural Networks, with a clear and colorful layout that appeals to those interested in data science and technology.

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

8 Questions2 MinutesCreated by LearningTree321

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

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

Evaluation of classifiers

Evaluate Your Classifier Knowledge

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