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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
•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
•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.
•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.
•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.