Databricks Certified Machine Learning - Associate
Get started today
Ultimate access to all questions.
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.
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.
Explanation:
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.