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Developing Regression Models with the Python Machine Learning Client for SAP HANA
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Introducing SAP HANA Machine Learning
Introducing SAP HANA Machine Learning
Setting Up the Environment
30 min
Exploring Data with SAP HANA DataFrames
30 min
Quiz
Building and Evaluating Regression Models
Training a Regression Model with SAP HANA PAL
20 min
Understanding Model Evaluation and Optimization
25 min
Quiz
Introducing SAP HANA Machine Learning
Setting Up the Environment
30 min
Exploring Data with SAP HANA DataFrames
30 min
Quiz
Building and Evaluating Regression Models
Training a Regression Model with SAP HANA PAL
20 min
Understanding Model Evaluation and Optimization
25 min
Quiz
Knowledge quiz
It's time to put what you've learned to the test, get 4 right to pass this unit.
1.
How does hana-ml connect to SAP HANA for accessing and processing data?
Choose the correct answer.
By using a proprietary SAP communication protocol
Through a REST API interface provided by SAP HANA Cloud
By exporting data to a local CSV file first
Through the SAP HANA Python driver (hdbcli)
2.
What type of plot is used to visualize the distribution of the 'Population' feature in the California Housing dataset?
Choose the correct answer.
Distribution plot
Scatter plot
Line plot
Pie chart
3.
What is the first step in establishing a connection to an SAP HANA Cloud instance using the SAP hana-ml class hana_ml.dataframe.ConnectionContext?
Choose the correct answer.
Import the pandas library
Set up an encrypted connection using the class 'SAP class hana_ml.dataframe.ConnectionContext'
Load the California housing dataset into memory
Execute a SQL query on the database
4.
Which components are part of the hana-ml package that enable machine learning within SAP HANA Cloud?
There are three correct answers.
Predictive Analysis Library (PAL) package
SAP HANA Cloud SQL Console
Automated Predictive Library (APL) package
SAP HANA DataFrame
5.
Which of the following functionalities are core to SAP HANA Cloud?
There are three correct answers.
Data storage and retrieval
Real-time analytics and processing
Limited support for machine learning models
Cloud-based deployment and scalability