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NEW QUESTION # 28
Which statement is true regarding decision trees and models based on ensembles of trees?
- A. For a Forest model, the out-of-bag sample is simply the original validation data set from when the raw data partitioning took place.
- B. In the gradient boosting algorithm, for all but the first iteration, the target is the residual from the previous decision tree model.
- C. A single decision tree will always be outperformed by a model based on an ensemble of trees.
- D. In the Forest algorithm, each individual tree is pruned based on using minimum Average Squared Error.
Answer: B
NEW QUESTION # 29
Which metric is commonly used to evaluate the performance of a regression model?
- A. F1 Score
- B. Mean Absolute Error (MAE)
- C. Confusion Matrix
- D. Precision
Answer: B
NEW QUESTION # 30
What is the main advantage of ensemble learning methods, such as Random Forest, in a machine learning pipeline?
- A. They are simple and easy to interpret.
- B. They are not suitable for large datasets.
- C. They require minimal data preprocessing.
- D. They combine multiple models to improve predictive performance.
Answer: D
NEW QUESTION # 31
Which algorithm is commonly used for decision-making tasks in classification models?
- A. Decision Trees
- B. K-Means
- C. Linear Regression
- D. Principal Component Analysis (PCA)
Answer: A
NEW QUESTION # 32
In a supervised machine learning pipeline, what is the purpose of the test data set?
- A. To train the machine learning model
- B. To evaluate the model's predictions
- C. To preprocess the data
- D. To validate the model's performance
Answer: D
NEW QUESTION # 33
In the context of data sources, what is ETL?
- A. Execute, Terminate, Launch
- B. Extract, Transform, Load
- C. Efficient Text Link
- D. Examine, Test, Log
Answer: B
NEW QUESTION # 34
What is metadata in the context of data sources?
- A. Data that is stored in a physical format
- B. Data that is encrypted for security
- C. Data about data, providing information such as data source, structure, and context
- D. Data that is in a non-standard, proprietary format
Answer: C
NEW QUESTION # 35
Which machine learning technique is typically used for building a model to predict a numeric target variable?
- A. Classification
- B. Clustering
- C. Dimensionality reduction
- D. Regression
Answer: D
NEW QUESTION # 36
Which technique is used for feature selection in a machine learning pipeline when dealing with a large number of features?
- A. One-Hot Encoding
- B. Regularization
- C. Naive Bayes
- D. Principal Component Analysis (PCA)
Answer: B
NEW QUESTION # 37
In model evaluation, what is the purpose of a ROC curve (Receiver Operating Characteristic)?
- A. To measure feature importance
- B. To evaluate the mean squared error of a model
- C. To visualize data distribution
- D. To compare models' performance in terms of sensitivity and specificity
Answer: D
NEW QUESTION # 38
When deploying a machine learning model, what is "model drift"?
- A. A measure of feature importance
- B. A sudden increase in the model's accuracy
- C. A change in the distribution of the input data or target variable over time
- D. The process of feature extraction
Answer: C
NEW QUESTION # 39
What is the purpose of hyperparameter tuning in a machine learning pipeline?
- A. To evaluate the model's predictions
- B. To select the most important features
- C. To train the model
- D. To optimize the model's hyperparameters for better performance
Answer: D
NEW QUESTION # 40
Which of the following metrics is commonly used to evaluate the performance of a binary classification model in a machine learning pipeline?
- A. Root Mean Squared Error (RMSE)
- B. Mean Absolute Error (MAE)
- C. Accuracy
- D. R-squared
Answer: C
NEW QUESTION # 41
Which technique is commonly used for feature scaling or normalization in machine learning pipelines?
- A. One-Hot Encoding
- B. Decision Trees
- C. Standardization
- D. Principal Component Analysis (PCA)
Answer: C
NEW QUESTION # 42
What is the purpose of cross-entropy loss in machine learning, especially in the context of classification?
- A. To measure the dissimilarity between predicted and actual class probabilities
- B. To evaluate feature importance
- C. To quantify the variance of a model
- D. To calculate the mean squared error of a regression model
Answer: A
NEW QUESTION # 43
In reinforcement learning, what is the agent's objective?
- A. To make predictions
- B. To generate synthetic data
- C. To learn from labeled data
- D. To maximize a cumulative reward over time
Answer: D
NEW QUESTION # 44
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