A. Replacing missing values with zeros
B. Ignoring missing data
C. Imputing missing values
D. Deleting rows with missing data
A. To summarize the distribution of target variables
B. To compute the mean squared error
C. To evaluate model performance, especially for binary classification
D. To visualize the data
A. Online news websites
B. Social media platforms
C. Weather stations
D. Stock market APIs
A. Model complexity
B. Model accuracy
C. Precision and recall
D. Data distribution
A. Data preprocessing
B. Model deployment
C. Training a model
D. Overfitting and generalization
A. One-Hot Encoding
B. Tokenization
C. Principal Component Analysis (PCA)
D. Standardization
A. Visualizing data
B. Optimizing the model's hyperparameters for better performance
C. Selecting the most important features
D. Training the model
A. To increase model complexity
B. To speed up model training
C. To add more features to the model
D. To prevent overfitting and reduce model complexity
A. The wider bars represent nodes with a higher probability of event.
B. The top bar represents the node with the highest count.
C. The top bar represents the node with the highest probability of event.
D. The darker bars represent nodes with a lower probability of event.
A. Data transformation and cleaning
B. Data exploration and visualization
C. Real-time data analysis
D. Long-term data storage and analysis
