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What is the primary challenge in implementing unsupervised learning as compared to supervised learning?

  • Difficulty in validation
  • Lack of rewards
  • Requires more data
  • Uses only labeled data
The primary challenge in unsupervised learning is the difficulty in validation since there are no predefined labels to assess the model's accuracy.
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You've noticed that changing the Epsilon value drastically changes the clustering results in your DBSCAN model. What strategies could you employ to select an optimal value?

  • Choose Epsilon randomly
  • Set Epsilon to a fixed value across all datasets
  • Use the 'k-distance graph'
  • Use trial and error
The 'k-distance graph' is a common method used to select the optimal Epsilon value in DBSCAN. By plotting the distance to the kth nearest neighbor for each point, you can identify an inflection point that represents an optimal balance between cluster density and granularity, helping you to choose an appropriate Epsilon value.
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In Gradient Boosting, the learning rate, also known as the __________ rate, controls the contribution of each tree to the final prediction.

  • Boosting
  • Growing
  • Shrinkage
  • nan
The learning rate in Gradient Boosting is often referred to as the shrinkage rate, controlling the contribution of each tree to the final prediction. A smaller learning rate means each tree has a smaller influence, leading to a more robust model.
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Suppose you have hierarchical data and need to understand the relationships between different parts. How would you approach clustering in this context?

  • Use DBSCAN
  • Use Hierarchical Clustering
  • Use K-Means
  • Use Mean Shift
Hierarchical Clustering is well-suited for understanding relationships within hierarchical data, as it creates a tree-like structure representing data hierarchies.
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The technique called ___________ can be used for nonlinear dimensionality reduction, providing a way to reduce dimensions while preserving the relationships between instances.

  • PCA
  • clustering
  • normalization
  • t-SNE
t-SNE (t-distributed Stochastic Neighbor Embedding) is a technique used for nonlinear dimensionality reduction. It's effective at preserving the relationships between instances in the reduced space, making it suitable for complex datasets where linear methods like PCA might fail.
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What does Precision measure in classification problems?

  • False Positives / Total predictions
  • True Negatives / (True Negatives + False Positives)
  • True Positives / (True Positives + False Negatives)
  • True Positives / (True Positives + False Positives)
Precision is the ratio of true positive predictions to the sum of true positives and false positives. It focuses on the accuracy of the positive predictions and is particularly important when the cost of false positives is high.
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In the field of agriculture, Machine Learning can be applied for ____________ optimization and disease prediction.

  • Crop Yield
  • Fraud Detection
  • Text Classification
  • Traffic Flow
Machine Learning can be applied in agriculture for Crop Yield Optimization, analyzing various factors like soil, weather, and irrigation to predict and improve crop output.
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Reinforcement learning involves an agent interacting with an environment through actions and receiving __________ as feedback.

  • accuracy
  • loss
  • penalties
  • rewards and penalties
Reinforcement learning uses both rewards and penalties as feedback to guide the learning process.
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If a model's errors have many outliers, the ________ may be significantly larger than the ________.

  • MAE, RMSE
  • MSE, MAE
  • R-Squared, Adjusted R-Squared
  • RMSE, MAE
If a model's errors have many outliers, the Root Mean Squared Error (RMSE) may be significantly larger than the Mean Absolute Error (MAE). RMSE is sensitive to larger errors, and outliers will have a pronounced effect on this metric. In contrast, MAE is less sensitive to outliers, leading to a smaller value in the presence of such errors.
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You implemented the KNN algorithm, and the model is performing poorly. What are the parameters you would tune, and how would you approach choosing the optimal K and distance metric?

  • Increase K and use Euclidean distance
  • Reduce dimensions and use any distance metric
  • Use cross-validation to find optimal K and distance metric
  • Use the same K for all datasets
Utilizing cross-validation helps in finding the optimal value of K and selecting an appropriate distance metric, leading to improved performance in KNN.
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How would you validate the quality of clusters formed in a given dataset?

  • By Using Metrics Like Silhouette Score
  • By the Number of Clusters Formed
  • Only by Visual Inspection
  • Through Specific Algorithms Like DBSCAN
The quality of clusters can be validated by using various metrics such as Silhouette Score, Davies–Bouldin Index, etc., which evaluate how well the data points are grouped within clusters and separated between different clusters.
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Consider a scenario where you need to combine supervised and unsupervised techniques. What might be a use case for semi-supervised learning?

  • Classification with abundant labeled data
  • Classification with limited labeled data
  • Clustering without labels
  • Real-time decision-making
Semi-Supervised Learning is particularly useful for classification tasks when there are limited labeled data, combining strengths of supervised and unsupervised techniques.
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