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Developing Regression Models with the Python Machine Learning Client for SAP HANA
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Building and Evaluating Regression Models
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.
What is one of the key advantages of using the Hybrid Gradient Boosting Tree (HGBT) algorithm for regression tasks?
Choose the correct answer.
It automatically normalizes all feature values for consistency
It is restricted to tasks involving only binary classification
It can handle both continuous and categorical features as input
It exclusively supports categorical data types
2.
Which parameter of the Hybrid Gradient Boosting Tree (HGBT) algorithm controls the maximum depth of each tree?
Choose the correct answer.
Learning rate
Subsample ratio
Max depth
Number of estimators
3.
Imagine you are working on improving a machine learning model and decide to re-train it. After re-training, you notice a change in the feature importance scores. What does a change in feature importance scores indicate?
Choose the correct answer.
A change in each feature's impact on predictions
An automatic increase in the model's accuracy
A reduction in correlations among the features
An equal influence of all features on predictions
4.
What does R2 (R squared) represent in a regression model?
Choose the correct answer.
The proportion of variability explained by the model
The predicted value of the dependent variable
The average error between predicted and actual values
The correlation between dependent and independent variables
5.
Which of the following are valid scenarios for using the Hybrid Gradient Boosting Tree (HGBT) algorithm in SAP HANA for regression?
There are three correct answers.
Predicting house prices based on various socioeconomic features
Classifying customer feedback as positive or negative
Forecasting sales figures based on historical data
Grouping customers into clusters based on purchase history