What is the impact of pruning on the bias-variance tradeoff in a Decision Tree model?
- Increases bias, reduces variance
- Increases both bias and variance
- Reduces bias, increases variance
- Reduces both bias and variance
Pruning a Decision Tree leads to a simpler model, which can increase bias but reduce variance. This tradeoff helps to avoid overfitting the training data and often results in a model that generalizes better to unseen data.
How does the Kernel Trick help in dealing with non-linear data in SVM?
- Enhances data visualization
- Maps data into higher-dimensional space for linear separation
- Reduces data size
- Speeds up computation
The Kernel Trick helps in dealing with non-linear data by mapping it into a higher-dimensional space where it can be linearly separated.
The F1-Score is the harmonic mean of _________ and _________.
- Accuracy, Recall
- Precision, Recall
- Precision, Specificity
- nan
The F1-Score is the harmonic mean of Precision and Recall. It gives equal weight to both these metrics, providing a balance between the ability to correctly identify positive cases and avoid false positives.
In a Multiple Linear Regression model, you discovered a significant interaction effect between two variables. How would you interpret this finding, and what implications might it have for the model?
- Add more variables
- Ignore the interaction
- No change to the model
- The effect of one variable depends on the level of the other
A significant interaction effect indicates that the effect of one variable on the response depends on the level of another variable. This means that the relationship between variables is not simply additive, and it may require the inclusion of an interaction term in the model to capture this complex relationship accurately.
In the context of a Confusion Matrix, _________ represents the cases where the model correctly predicted the negative class.
- False Negatives
- False Positives
- True Negatives
- True Positives
True Negatives (TN) in a Confusion Matrix represent cases where the model correctly predicted the negative class. It indicates that the negative instances were classified correctly.
You are given a complex dataset with a large amount of unstructured data. Which among AI, Machine Learning, or Deep Learning would be best suited to analyze this, and why?
- AI, for its simplicity
- Deep Learning, for its ability to handle complex and unstructured data
- Machine Learning, for its structured data analysis
- nan
Deep Learning models are adept at handling unstructured data and finding complex patterns, making them suitable for such a dataset.
How does choosing the value of K in the K-Nearest Neighbors (KNN) algorithm impact the decision boundary?
- Both 1 & 2 depending on value
- Makes it more complex
- Makes it smoother
- nan
A smaller K value results in a more complex decision boundary, while a larger K value makes it smoother.
How does the Elbow Method determine the optimal number of clusters, and what are its limitations?
- By evaluating the model's accuracy
- By finding the point of maximum curvature on a plot of variance vs. clusters
- By maximizing the cluster distances
- By minimizing the inter-cluster distances
The Elbow Method determines the optimal number of clusters by finding the "elbow" point on a plot of variance vs. clusters. Limitations include ambiguity in identifying the exact "elbow" and sensitivity to initialization.
The percentage of total variance explained by a principal component in PCA can be calculated by dividing the Eigenvalue of that component by the ________.
- magnitude of Eigenvectors
- number of Eigenvectors
- number of components
- sum of all Eigenvalues
The percentage of total variance explained by a principal component is calculated by dividing its Eigenvalue by the "sum of all Eigenvalues." This ratio gives the proportion of the dataset's total variance that is captured by that specific component.
In ElasticNet regularization, the mixing parameter 'alpha' balances the effects of ________ and ________.
- L1, L2
- L1, L3
- L2, L3
- nan
The 'alpha' parameter in ElasticNet regularization balances the effects of L1 and L2 penalties, providing a compromise between Ridge and Lasso.