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Which of the following statements describes a Spark ML estimator?
In the context of Spark MLlib, an estimator refers to an algorithm which can be 'fit' on a DataFrame to produce a model (referred to as a Transformer), which can then be used to transform one DataFrame into another, typically adding predictions or model scores. This is a fundamental concept in machine learning pipelines in Spark, where the workflow includes fitting estimators to data to produce transformers.
Reference
Spark MLlib Documentation: https://spark.apache.org/docs/latest/ml-pipeline.html#estimators
A data scientist is developing a single-node machine learning model. They have a large number of model configurations to test as a part of their experiment. As a result, the model tuning process takes too long to complete. Which of the following approaches can be used to speed up the model tuning process?
To speed up the model tuning process when dealing with a large number of model configurations, parallelizing the hyperparameter search using Hyperopt is an effective approach. Hyperopt provides tools like SparkTrials which can run hyperparameter optimization in parallel across a Spark cluster.
Example:
from hyperopt import fmin, tpe, hp, SparkTrials search_space = { 'x': hp.uniform('x', 0, 1), 'y': hp.uniform('y', 0, 1) } def objective(params): return params['x'] ** 2 + params['y'] ** 2 spark_trials = SparkTrials(parallelism=4) best = fmin(fn=objective, space=search_space, algo=tpe.suggest, max_evals=100, trials=spark_trials)
Hyperopt Documentation
A data scientist has defined a Pandas UDF function predict to parallelize the inference process for a single-node model:
They have written the following incomplete code block to use predict to score each record of Spark DataFrame spark_df:
Which of the following lines of code can be used to complete the code block to successfully complete the task?
To apply the Pandas UDF predict to each record of a Spark DataFrame, you use the mapInPandas method. This method allows the Pandas UDF to operate on partitions of the DataFrame as pandas DataFrames, applying the specified function (predict in this case) to each partition. The correct code completion to execute this is simply mapInPandas(predict), which specifies the UDF to use without additional arguments or incorrect function calls. Reference:
PySpark DataFrame documentation (Using mapInPandas with UDFs).
A data scientist has replaced missing values in their feature set with each respective feature variable's median value. A colleague suggests that the data scientist is throwing away valuable information by doing this.
Which of the following approaches can they take to include as much information as possible in the feature set?
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.
Reference
'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
What is the name of the method that transforms categorical features into a series of binary indicator feature variables?
The method that transforms categorical features into a series of binary indicator variables is known as one-hot encoding. This technique converts each categorical value into a new binary column, which is essential for models that require numerical input. One-hot encoding is widely used because it helps to handle categorical data without introducing a false ordinal relationship among categories. Reference:
Feature Engineering Techniques (One-Hot Encoding).
