Examples of machine learning models for streaming data

📚 Quick Study Guide

• ⏱️ Streaming data requires models that can learn and adapt in real-time.
• 📈 Key algorithms include online learning methods, which update models incrementally.
• 📊 Common evaluation metrics are based on sliding windows to assess performance over time.
• ⚙️ Drift detection is crucial to identify and respond to changes in data characteristics.
• 💡 Feature engineering is essential for adapting to changing data patterns.

🧪 Practice Quiz

1. Which of the following is a characteristic of machine learning models designed for streaming data?
   1. A. Batch processing of data
   2. B. Ability to update models incrementally
   3. C. Static model parameters
   4. D. Reliance on complete datasets
2. What is a key challenge when applying machine learning to streaming data?
   1. A. The absence of labeled data
   2. B. Limited computational resources
   3. C. Concept drift
   4. D. High data storage costs
3. Which algorithm is commonly used for online learning in streaming data scenarios?
   1. A. K-Means Clustering
   2. B. Support Vector Machines (SVM) with batch updates
   3. C. Stochastic Gradient Descent (SGD)
   4. D. Principal Component Analysis (PCA)
4. How is the performance of a machine learning model evaluated on streaming data?
   1. A. Using a fixed test set
   2. B. Averaging performance across the entire dataset
   3. C. Using a sliding window to evaluate performance over time
   4. D. Evaluating performance only at the end of the stream
5. What does 'concept drift' refer to in the context of streaming data?
   1. A. Changes in the hardware used for data processing
   2. B. Shifts in the statistical properties of the data over time
   3. C. Errors in the data collection process
   4. D. Variations in the programming language used for model training
6. Which technique is helpful in addressing concept drift?
   1. A. Using a static model trained on initial data
   2. B. Periodically retraining the model on new data
   3. C. Decreasing the learning rate over time
   4. D. Ignoring changes in data patterns
7. Which of the following is an example of a model suited for streaming data?
   1. A. Batch Gradient Descent
   2. B. Hoeffding Tree
   3. C. K-Nearest Neighbors (KNN) with a fixed dataset
   4. D. Standard Decision Tree