A. Building a machine learning model from scratch
B. Creating new features from existing data
C. Applying the model to new data
D. Testing the model's performance
A. Mean Absolute Error (MAE)
B. Confusion Matrix
C. F1 Score
D. Precision
A. To maximize the model's performance
B. To measure the difference between predicted and actual values
C. To visualize the data
D. To assess the accuracy of the model
A. The evaluation of data distribution
B. The process of data preprocessing
C. The process of selecting features
D. The periodic assessment of a deployed model's performance and potential retraining
A. Logi_rfm12
B. Logi_rfm4
C. Logi_rfm6
D. Logi_rfm8
A. To assess data quality
B. To evaluate the model's accuracy
C. To create synthetic data
D. To compare two different versions of a model or strategy to determine which performs better
A. High security features
B. Support for complex data structures
C. Real-time data processing
D. Simple and standardized communication
A. The type of data used
B. The presence or absence of a target variable
C. The use of feature engineering
D. The amount of labeled data required
A. Making predictions or inferences from data
B. Feature engineering
C. Data cleaning
D. Data visualization
A. Online news websites
B. Social media platforms
C. Weather stations
D. Stock market APIs
