CS 326 CORE EXAMS SET 2025/2026 QUESTIONS AND
SOLUTION RATED A+
✔✔Decision Trees - ✔✔Models that split data based on feature values to make
predictions.
✔✔Ensemble Methods - ✔✔Techniques that combine multiple models for better
accuracy.
✔✔Bagging - ✔✔Combines predictions from multiple models to reduce variance (e.g.,
Random Forest).
✔✔Boosting - ✔✔Combines weak learners to form a strong learner (e.g., AdaBoost,
Gradient Boosting).
✔✔Support Vector Machines (SVM) - ✔✔Finds the optimal hyperplane to separate
classes.
✔✔Neural Networks - ✔✔Consist of layers of interconnected nodes (neurons) for
complex pattern recognition.
✔✔Hyperparameter Tuning - ✔✔Optimizing model parameters to improve performance.
✔✔Docker - ✔✔A platform for developing, shipping, and running applications in
containers.
✔✔Containers - ✔✔Lightweight, portable units that package applications and
dependencies.
✔✔Benefits of Docker - ✔✔Ensures consistency across environments, scalability, and
isolation.
✔✔Clustering - ✔✔An unsupervised learning technique for grouping similar data points.
✔✔K-Means Clustering - ✔✔Partitions data into K clusters by minimizing within-cluster
variance.
✔✔Hierarchical Clustering - ✔✔Builds a tree of clusters based on similarity.
✔✔Clustering Evaluation Metrics - ✔✔Includes Silhouette score and Davies-Bouldin
index.
✔✔Recommendation Systems - ✔✔Suggest items to users based on preferences.
SOLUTION RATED A+
✔✔Decision Trees - ✔✔Models that split data based on feature values to make
predictions.
✔✔Ensemble Methods - ✔✔Techniques that combine multiple models for better
accuracy.
✔✔Bagging - ✔✔Combines predictions from multiple models to reduce variance (e.g.,
Random Forest).
✔✔Boosting - ✔✔Combines weak learners to form a strong learner (e.g., AdaBoost,
Gradient Boosting).
✔✔Support Vector Machines (SVM) - ✔✔Finds the optimal hyperplane to separate
classes.
✔✔Neural Networks - ✔✔Consist of layers of interconnected nodes (neurons) for
complex pattern recognition.
✔✔Hyperparameter Tuning - ✔✔Optimizing model parameters to improve performance.
✔✔Docker - ✔✔A platform for developing, shipping, and running applications in
containers.
✔✔Containers - ✔✔Lightweight, portable units that package applications and
dependencies.
✔✔Benefits of Docker - ✔✔Ensures consistency across environments, scalability, and
isolation.
✔✔Clustering - ✔✔An unsupervised learning technique for grouping similar data points.
✔✔K-Means Clustering - ✔✔Partitions data into K clusters by minimizing within-cluster
variance.
✔✔Hierarchical Clustering - ✔✔Builds a tree of clusters based on similarity.
✔✔Clustering Evaluation Metrics - ✔✔Includes Silhouette score and Davies-Bouldin
index.
✔✔Recommendation Systems - ✔✔Suggest items to users based on preferences.
