Which of the following is the best statement:
Generally speaking, most ML algorithms deliver similar performance on a data set.
Data quality, rather than ML algorithm capability, is likely the more important driver of ML project success.
Out-of-the-box Weka and Driverless AI solutions are generally similar.
Traditional ML and AutoML workflows require similar effort and produce similar outcomes.
AutoML uses brute force experimentation to develop a final model pipeline.
The best statement is - Data quality, rather than ML algorithm capability, is likely the more important driver of ML project success.
Poor training data quality is no good for your machine learning model. Until you enter the correct data, your artificial intelligence (AI) or machine learning (ML) model will not give you accurate results.
If you train a computer vision system with incomplete datasets, it can have disastrous results in some AI-enabled models, such as driving an autonomous vehicle.
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