APPLICATION OF SUPERVISED LEARNING
MACHINE LEARNING PIPELINE
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Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
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You are working with a machine learning team training an image classification model using MXNet on Amazon SageMaker. The requirements state that the model should be at least 85% accurate. The data appears to be of good quality, but the accuracy is around 48% during training with the test data. Most of the time wrong labels are being predicted. What should be done to help increase the accuracy of the model?
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Use Amazon SageMaker’s automatic model tuning. Take the best performing hyperparameters and run multiple training jobs in parallel using Apache Spark and Spark ML
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Use Amazon SageMaker’s automatic model tuning. Specify the objective metric and take the best performing parameters suggested by the service to use when training the model
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Use Amazon SageMaker’s automatic model tuning. Use AWS Batch to run multiple batches of the training data with different hyper parameters specified during the autotuning job.
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Use Amazon SageMaker’s automatic model tuning. Take the best performing hyperparameters and manually adjust them to meet your requirements.
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Explanation:
Detailed explanation-1: -Random Forest Classifier shows the best performance with 47% accuracy followed by KNN with 34% accuracy, NB with 30% accuracy, and Decision Tree with 27% accuracy. Thus, Random Forest exhibits the best performance and Decision Tree the worst.
Detailed explanation-2: -The recommended input format for the Amazon SageMaker object detection algorithms is Apache MXNet RecordIO . However, you can also use raw images in . jpg or . png format.
Detailed explanation-3: -A manifest file contains information about the images and image labels that you can use to train and test a model. You can store a manifest file in an Amazon S3 bucket and use it to create a dataset.
There is 1 question to complete.