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The implementation of linear regression in Spark ML first attempts to solve the linear regression problem using matrix decomposition, but this method does not scale well to large datasets with a large number of variables.
Which of the following approaches does Spark ML use to distribute the training of a linear regression model for large data?
For large datasets, Spark ML uses iterative optimization methods to distribute the training of a linear regression model. Specifically, Spark MLlib employs techniques like Stochastic Gradient Descent (SGD) and Limited-memory Broyden--Fletcher--Goldfarb--Shanno (L-BFGS) optimization to iteratively update the model parameters. These methods are well-suited for distributed computing environments because they can handle large-scale data efficiently by processing mini-batches of data and updating the model incrementally.
Databricks documentation on linear regression: Linear Regression in Spark ML
A data scientist has been given an incomplete notebook from the data engineering team. The notebook uses a Spark DataFrame spark_df on which the data scientist needs to perform further feature engineering. Unfortunately, the data scientist has not yet learned the PySpark DataFrame API.
Which of the following blocks of code can the data scientist run to be able to use the pandas API on Spark?
To use the pandas API on Spark, the data scientist can run the following code block:
import pyspark.pandas as ps df = ps.DataFrame(spark_df)
This code imports the pandas API on Spark and converts the Spark DataFrame spark_df into a pandas-on-Spark DataFrame, allowing the data scientist to use familiar pandas functions for further feature engineering.
Databricks documentation on pandas API on Spark: pandas API on Spark
A data scientist has developed a linear regression model using Spark ML and computed the predictions in a Spark DataFrame preds_df with the following schema:
prediction DOUBLE
actual DOUBLE
Which of the following code blocks can be used to compute the root mean-squared-error of the model according to the data in preds_df and assign it to the rmse variable?
A)
B)
C)
D)
E)
The code block to compute the root mean-squared error (RMSE) for a linear regression model in Spark ML should use the RegressionEvaluator class with metricName set to 'rmse'. Given the schema of preds_df with columns prediction and actual, the correct evaluator setup will specify predictionCol='prediction' and labelCol='actual'. Thus, the appropriate code block (Option C in your list) that uses RegressionEvaluator to compute the RMSE is the correct choice. This setup correctly measures the performance of the regression model using the predictions and actual outcomes from the DataFrame. Reference:
Spark ML documentation (Using RegressionEvaluator to Compute RMSE).
A health organization is developing a classification model to determine whether or not a patient currently has a specific type of infection. The organization's leaders want to maximize the number of positive cases identified by the model.
Which of the following classification metrics should be used to evaluate the model?
When the goal is to maximize the identification of positive cases in a classification task, the metric of interest is Recall. Recall, also known as sensitivity, measures the proportion of actual positives that are correctly identified by the model (i.e., the true positive rate). It is crucial for scenarios where missing a positive case (false negative) has serious implications, such as in medical diagnostics. The other metrics like Precision, RMSE, and Accuracy serve different aspects of performance measurement and are not specifically focused on maximizing the detection of positive cases alone. Reference:
Classification Metrics in Machine Learning (Understanding Recall).
An organization is developing a feature repository and is electing to one-hot encode all categorical feature variables. A data scientist suggests that the categorical feature variables should not be one-hot encoded within the feature repository.
Which of the following explanations justifies this suggestion?
The suggestion not to one-hot encode categorical feature variables within the feature repository is justified because one-hot encoding can be problematic for some machine learning algorithms. Specifically, one-hot encoding increases the dimensionality of the data, which can be computationally expensive and may lead to issues such as multicollinearity and overfitting. Additionally, some algorithms, such as tree-based methods, can handle categorical variables directly without requiring one-hot encoding.
Databricks documentation on feature engineering: Feature Engineering
