BAquiz1Fin10_12

What is the main purpose of ensemble methods in predictive modeling?

• To reduce data preprocessing time

• To combine predictions from multiple models for improved accuracy

• To simplify models for easier interpretation

• To increase the size of the dataset

Which ensemble method builds multiple decision trees and merges them together to get a more accurate and stable prediction?

• Gradient Boosting

• Random Forest

• Logistic Regression

• K-Nearest Neighbors

What is the main technique used in Gradient Boosting?

• Combining multiple models

• Training each new model to correct the errors of the previous ones

• Using a single large model

• Randomly sampling the data

In Random Forest, what does the term "bagging" refer to?

• Combining multiple datasets

• Selecting random subsets of data and features for training

• Reducing the dimensionality of the data

• Increasing the size of the training dataset

Which of the following is a characteristic of neural networks?

• They can only handle structured data

• They rely on linear relationships

• They consist of layers of interconnected nodes (neurons)

• They are less complex than traditional algorithms

What does the term "overfitting" mean in the context of predictive modeling?

• When a model performs poorly on training data

• When a model learns noise in the training data instead of the underlying pattern

• When a model generalizes well to new data

• When a model is too simple

What is the role of activation functions in neural networks?

• To combine predictions from multiple models

• To adjust weights in the network

• To introduce non-linearity into the model

• To optimize the learning rate

Which of the following is a popular library for building neural networks in Python?

• Scikit-learn

• Matplotlib

• TensorFlow

• NumPy

In the context of ensemble methods, what does the term "out-of-bag error" refer to?

• Error rate calculated on data not used during training

• Error rate on the training dataset

• Error rate of the best-performing model

• Error rate of models during cross-validation

Which technique is commonly used to prevent overfitting in neural networks?

• Increasing the learning rate

• Using regularization techniques (e.g., dropout)

• Reducing the size of the dataset

• Increasing the number of epochs