A. Classification predicts categorical outcomes, while regression predicts numeric outcomes.
B. Classification is a type of regression problem.
C. There is no difference; the terms are used interchangeably.
D. Regression predicts categorical outcomes, while classification predicts numeric outcomes.
A. It refers to the trade-off between the number of features and the model's complexity.
B. It is not relevant in machine learning.
C. It indicates the trade-off between accuracy and precision.
D. It represents the trade-off between underfitting and overfitting.
A. Rules for model comparison statistic
B. Partition Data percentages
C. Advisor Options for missing values
D. Event-based Sampling proportions
A. To validate the model's performance
B. To test the model's generalization capability
C. To train the machine learning model
D. To evaluate the model's predictions
A. Random Forest
B. Linear Regression
C. Support Vector Machine (SVM)
D. K-Means Clustering
A. A specialized database for time-series data
B. A centralized repository for storing all structured and unstructured data at any scale
C. A data storage solution designed for high-speed data retrieval
D. A backup system for relational databases
A. Autoencoder
B. Robust PCA
C. Singular value decomposition
D. Principal component analysis
A. One-Hot Encoding
B. Tokenization
C. Principal Component Analysis (PCA)
D. Standardization
A. A single decision tree will always be outperformed by a model based on an ensemble of trees.
B. For a Forest model, the out-of-bag sample is simply the original validation data set from when the raw data partitioning took place.
C. In the Forest algorithm, each individual tree is pruned based on using minimum Average Squared Error.
D. In the gradient boosting algorithm, for all but the first iteration, the target is the residual from the previous decision tree model.
A. Grouping similar words together based on their meanings
B. Reducing words to their base or root form
C. Creating new words to improve model performance
D. Converting text to numbers for model input
