One of the challenges with Gradient Boosting is its sensitivity to _______ parameters, which can affect the model's performance.
- Hyperparameters
- Feature selection
- Model architecture
- Data preprocessing
Gradient Boosting is indeed sensitive to hyperparameters like the learning rate, tree depth, and the number of estimators. These parameters need to be carefully tuned to achieve optimal model performance. Hyperparameter tuning is a critical step in using gradient boosting effectively.
Loading...
Related Quiz
- In deep learning, the technique used to skip one or more layers by connecting non-adjacent layers is called _______.
- In L2 regularization, the penalty is proportional to the _______ of the magnitude of the coefficients.
- When you want to visualize geographical data with customizable layers and styles, which tool is commonly used?
- In a Hadoop ecosystem, which tool is primarily used for data ingestion from various sources?
- _________ is a popular open-source framework used for real-time processing and analytics of large streams of data.