For a machine learning progress, how should you split data for training and evaluation?
For a machine learning progress, how should you split data for training and evaluation?
The correct approach to splitting data for training and evaluation in a machine learning process is to randomly divide the data into rows for training and rows for evaluation. This method ensures that both training and evaluation sets are representative of the overall dataset, thus helping to create a more accurate model. Randomly splitting into rows helps in mitigating any bias and ensures that the evaluation of the model's performance is reliable.
You split rows not columns: https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/split-data#how-to-configure-split-data
Answer should be B. https://docs.microsoft.com/en-us/azure/machine-learning/algorithm-module-reference/split-data
on exam 28/06/2024
This questions was there in my todays exam
B. Randomly split the data into rows for training and rows for evaluation.
Absolutely B!
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Someone from support can answer me. I bought the unlimited plan but I can't access other exams. and some exams are giving errors when I access them.
training vs evaluation ratio: 70% vs 30%
B is correct
B makes sense
B is correct
Split the data for training and testing. randomly 75-25 split on row may be a good idea.
on exam in July 2023
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B est correct
give answer is correct, you can randomly split rows in order to have a variable sample to train and to evaluate the model