Databricks Databricks-Machine-Learning-Associate Exam Dumps - Actual Questions Answers

In Spark ML, the linear regression model expects the feature column to be a vector type. However, if thefeaturescolumn in the DataFrametrain_dfis not already in this format (such as being a column of type UDT or a non-vectorized type), the engineerneeds to convert it to a vector column using a transformer likeVectorAssembler. This is a critical step in preparing the data for modeling as Spark ML models require input features to be combined into a single vector column.

References

• Spark MLlib documentation forLinearRegression:https://spark.apache.org/docs/latest/ml-classification-regression.html#linear-regression

Spark ML (Machine Learning Library) is designed specifically for handling large-scale data processing and machine learning tasks directly within Apache Spark. It provides tools and APIs for large-scale feature engineering without the need to rely on user-defined functions (UDFs) or pandas Function API, allowing for more scalable and efficient data transformations directly distributed across a Spark cluster. Unlike Keras, pandas, PyTorch, and scikit-learn, Spark ML operates natively in a distributed environment suitable for big data scenarios.References:

• Spark MLlib documentation (Feature Engineering with Spark ML).

By creating a binary feature variable for each feature with missing values to indicate whether a value has been imputed, the data scientist can preserve information about the original state of the data. This approach maintains the integrity of the dataset by marking which values are original and which are synthetic (imputed). Here are the steps to implement this approach:

• Identify Missing Values:Determine which features contain missing values.
• Impute Missing Values:Continue with median imputation or choose another method (mean, mode, regression, etc.) to fill missing values.
• Create Indicator Variables:For each feature that had missing values, add a new binary feature. This feature should be '1' if the original value was missing and imputed, and '0' otherwise.
• Data Integration:Integrate these new binary features into the existing dataset. This maintains a record of where data imputation occurred, allowing models to potentially weight these observations differently.
• Model Adjustment:Adjust machine learning models to account for these new features, which might involve considering interactions between these binary indicators and other features.

References

• "Feature Engineering for Machine Learning" by Alice Zheng and Amanda Casari (O'Reilly Media, 2018), especially the sections on handling missing data.
• Scikit-learn documentation on imputing missing values: https://scikit-learn.org/stable/modules/impute.html