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NEW QUESTION # 67
In which of the following situations is it preferable to impute missing feature values with their median value over the mean value?
Answer: D
Explanation:
Imputing missing values with the median is often preferred over the mean in scenarios where the data contains a lot of extreme outliers. The median is a more robust measure of central tendency in such cases, as it is not as heavily influenced by outliers as the mean. Using the median ensures that the imputed values are more representative of the typical data point, thus preserving the integrity of the dataset's distribution. The other options are not specifically relevant to the question of handling outliers in numerical data.
Reference:
Data Imputation Techniques (Dealing with Outliers).
NEW QUESTION # 68
A data scientist wants to efficiently tune the hyperparameters of a scikit-learn model. They elect to use the Hyperopt library's fmin operation to facilitate this process. Unfortunately, the final model is not very accurate. The data scientist suspects that there is an issue with the objective_function being passed as an argument to fmin.
They use the following code block to create the objective_function:
Which of the following changes does the data scientist need to make to their objective_function in order to produce a more accurate model?
Answer: B
Explanation:
When using the Hyperopt library with fmin, the goal is to find the minimum of the objective function. Since you are using cross_val_score to calculate the R2 score which is a measure of the proportion of the variance for a dependent variable that's explained by an independent variable(s) in a regression model, higher values are better. However, fmin seeks to minimize the objective function, so to align with fmin's goal, you should return the negative of the R2 score (-r2). This way, by minimizing the negative R2, fmin is effectively maximizing the R2 score, which can lead to a more accurate model.
Reference
Hyperopt Documentation: http://hyperopt.github.io/hyperopt/
Scikit-Learn documentation on model evaluation: https://scikit-learn.org/stable/modules/model_evaluation.html
NEW QUESTION # 69
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?
Answer: B
Explanation:
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.
Reference:
Databricks documentation on pandas API on Spark: pandas API on Spark
NEW QUESTION # 70
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?
Answer: E
Explanation:
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).
NEW QUESTION # 71
A machine learning engineer has grown tired of needing to install the MLflow Python library on each of their clusters. They ask a senior machine learning engineer how their notebooks can load the MLflow library without installing it each time. The senior machine learning engineer suggests that they use Databricks Runtime for Machine Learning.
Which of the following approaches describes how the machine learning engineer can begin using Databricks Runtime for Machine Learning?
Answer: D
Explanation:
The Databricks Runtime for Machine Learning includes pre-installed packages and libraries essential for machine learning and deep learning, including MLflow. To use it, the machine learning engineer can simply select an appropriate Databricks Runtime ML version from the "Databricks Runtime Version" dropdown menu while creating their cluster. This selection ensures that all necessary machine learning libraries, including MLflow, are pre-installed and ready for use, avoiding the need to manually install them each time.
Reference
Databricks documentation on creating clusters: https://docs.databricks.com/clusters/create.html
NEW QUESTION # 72
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