WeCP provides you with a unique test environment that can be used to evaluate the candidates using different question types. This article explains the Machine Learning question type in detail.

These questions are specific and focused on assessing candidates' sound knowledge in machine learning concepts, the accuracy of the model trained by the candidate, and the ability of the candidate to handle datasets of variable sizes.

You can assess the candidates by providing them with pre-existing test and train datasets on the platform. WeCP also provides ready-made Machine Learning assessments to help your hiring needs meet the evaluation metrics.

These machine Learning assessments would require you to provide specific instructions about the requirements of the model, details about the algorithms that must be used while training the model, and the metrics that will be used to evaluate the final predictions.

The accuracy of all such Machine Learning questions is automatically evaluated by WeCP using statistical techniques like F1-score, RMSE, Accuracy Score, ROC/AUC Curve, etcetera, once the final prediction is uploaded by the candidate.

The performance score is determined by comparing the candidate's model with a pre-existing dataset that is perfectly modeled, corresponding to each question.

After performing the required tasks, the candidates are needed to upload the required file to get their answers evaluated by the platform.

WeCP also provides you with options to enable plagiarism prevention and fraud detection in your tests. These proctor settings play a major role in assisting you in picking the right candidate.
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The candidates are required to upload the file in .csv format, as specified in the question.
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Read More What are the different question types supported by WeCP?
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