You need to build a recommendation system with a clear explanation of why certain recommendations are made. How would you approach this considering AI, Machine Learning, and Deep Learning?
- AI, for its rule-based logic
- Deep Learning, for its data-driven insights
- Machine Learning, for its predictive analysis
- nan
Traditional AI models, often based on rule-based logic, may provide clear explanations for recommendations, whereas Machine Learning and Deep Learning models may be less interpretable.
Can you explain how the choice of kernel in SVM affects the decision boundary?
- Changes the color
- Increases computation time
- Reduces data size
- Transforms the feature space
Different kernels transform the feature space in different ways, affecting the shape and position of the decision boundary in SVM.
Clustering is a common task in __________ learning, where data is grouped based on inherent similarities without the use of labels.
- reinforcement
- semi-supervised
- supervised
- unsupervised
Unsupervised learning commonly involves clustering, where data is grouped based on similarities without using labels.
What is the primary difference between the Gini Index and entropy when used in Decision Trees?
- Calculation Method
- Complexity
- Scale
- Units
Gini Index and entropy are both used to measure purity, but they are calculated differently. Entropy uses logarithms, while Gini Index does not.
How does DBSCAN handle noise in the data, and what distinguishes it from other clustering methods?
- Classifies Noise as a Separate Cluster
- Considers Noise in Cluster Formation
- Handles Noise Through Density-Based Clustering
- Ignores Noise
DBSCAN handles noise by classifying it as a separate category and distinguishes itself by utilizing a density-based approach that groups together points that are closely packed, considering the rest as noise.
In the context of regression analysis, ________ accounts for the number of predictors in the model and adjusts the R-Squared accordingly.
- Adjusted R-Squared
- MAE
- R-Squared
- RMSE
Adjusted R-Squared accounts for the number of predictors in the model, providing a more balanced view of model fit by penalizing the inclusion of irrelevant features. It adjusts the R-Squared value based on the number of predictors, making it more suitable for comparing models with different numbers of variables.
What is a binary classification problem?
- A problem with two algorithms
- A problem with two classes or outcomes
- A problem with two input variables
- A problem with two solutions
A binary classification problem involves predicting one of two possible classes or outcomes. It's a specific type of classification where the target variable has only two possible values.
What are the potential issues with using a large value of K in KNN, and how can they be mitigated?
- All of the above
- Complexity, can be mitigated by feature selection
- Overfitting, can be mitigated by reducing dimensions
- Underfitting, can be mitigated by reducing K
A large value of K in KNN may cause underfitting. It can be mitigated by reducing K or using techniques like cross-validation.
In a situation where the MAE is significantly lower than the RMSE, what might this tell you about the distribution of the errors in your model?
- Errors are normally distributed; no impact on model
- Errors are uniformly distributed; no large outliers
- Many large errors, few small outliers
- Many small errors, few large outliers
When the Mean Absolute Error (MAE) is significantly lower than the Root Mean Squared Error (RMSE), it likely indicates that the model has many small errors and a few large outliers. RMSE, being sensitive to larger errors, would be higher, while MAE would be less impacted by those larger errors. An analysis of the residuals can further elucidate the nature of these errors.
In the context of SVM, the support vectors are the data points that lie closest to the _________.
- Data center
- Hyperplane
- Kernel
- Margin
Support vectors are the data points that lie closest to the hyperplane and influence its position and orientation.