Verification and Evaluation

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Verification and Evaluation

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Verification and Evaluation

Validation and evaluation refers to Y-AI's plug-in operator, including four nodes: multi-class model performance evaluation, two-class model performance evaluation, regression model performance evaluation, and model application.

Multi-classifier Evaluation

Analyze the performance indicators of the multi-classification algorithm on the validation set

Parameter list: reserved bits for performance indicators

Output list: confusion matrix, performance indicators

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Bi-classifier Evaluation

Analyze the performance indicators of the two classification algorithm on the validation set

Parameter list: reserved bits of performance indicators, positive label

Output list: ROC curve, performance index

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Regression Evaluation

Performance indicators of regression prediction algorithm on validation set.

Parameter list: reserved bits for performance indicators.

Output list: performance indicators, comparison of real and predicted values.

ML213

Clustering Evaluation

Performance indicators of regression prediction algorithm on validation set.

Parameter list: reserved bits for performance indicators.

Output list: clustering performance evaluation, visualization of results after clustering.

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Apply Model

Apply the trained model to the data set to make predictions.

Parameter list: whether to output all columns, inverse transformation.

Output list: prediction results.

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