Understanding Classification with SAP HANA

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

Understanding Classification with SAP HANA

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 3 right to pass this unit.

There are two correct answers.

Always use domain knowledge for selecting features.

Consider the business problem before starting the model.

Ensure model testing is only done on the training dataset.

Use mean squared error (MSE) as the primary evaluation metric.

Choose the correct answer.

The overall accuracy of regression models or predictions.

The structural flow and logic of decision tree models.

The performance metrics of a classification algorithm.

The dimensional attributes of data for better insights.

There are three correct answers.

Precision to measure true positive predictions for a class.

Recall to determine sensitivity in detecting positive cases.

F1 score to balance precision and recall trade-offs.

RMSE to analyze the overall prediction error rates.