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Developing Classification Models with the Python Machine Learning Client for SAP HANA
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Training a PAL Classification Model for the Employee Churn dataset
Understanding Classification with SAP HANA
Providing an Overview of Classification Models
15 mins
Exploring the Demo Scenario - Preventing Employee Churn
10 mins
Quiz
Setting Up the Environment and Analyzing Data with the SAP HANA Dataframes
Training a PAL Classification Model for the Employee Churn dataset
Evaluating and Testing the Model
Knowledge quiz
It's time to put what you've learned to the test, get 2 right to pass this unit.
1.
What step is important for partitioning data for classification?
Choose the correct answer.
Handling missing data in the given dataset.
Normalizing or scaling feature values for consistency.
Splitting the data into training and testing subsets.
Building the model for training with classification tasks.
2.
Why is it essential to partition data during model preparation?
Choose the correct answer.
To ensure the model generalizes well on unseen testing data.
To reduce the overall size of the training dataset.
To improve prediction accuracy on the training dataset.
To minimize preprocessing steps like scaling or normalization.