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Answer: Use the `fit` method of the Spark ML estimator to train the model on the training data, and then use the `evaluate` method of the Spark ML model to evaluate the model's performance on the test data.
The correct approach to train and evaluate a machine learning model using Spark ML involves using the `fit` method of the Spark ML estimator to train the model on the training data, and then using the `evaluate` method of the Spark ML model to evaluate the model's performance on the test data. Option A is incorrect because the `transform` method is used to transform the data, not to make predictions. Option B is incorrect because training the model on the entire dataset can lead to overfitting. Option D is incorrect because `fitAndTransform` is used to train the model and transform the data in one step, but it does not evaluate the model's performance.
Author: LeetQuiz Editorial Team
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Consider a scenario where you have a dataset with 10 features and you want to train a machine learning model using Spark ML. Describe the steps involved in training and evaluating the model, and explain the role of Spark ML estimator and Spark ML transformer in this process. Provide a code snippet demonstrating the training and evaluation of a model using Spark ML.
A
Use the fit method of the Spark ML estimator to train the model on the training data, and then use the transform method of the Spark ML transformer to make predictions on the test data.
B
Use the fit method of the Spark ML estimator to train the model on the entire dataset, and then use the predict method of the Spark ML model to make predictions on the test data.
C
Use the fit method of the Spark ML estimator to train the model on the training data, and then use the evaluate method of the Spark ML model to evaluate the model's performance on the test data.
D
Use the fitAndTransform method of the Spark ML estimator to train the model and make predictions on the entire dataset, and then use the evaluate method of the Spark ML model to evaluate the model's performance.
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