Which of the following is a concern when machine learning models make decisions without human understanding: Accuracy, Scalability, Interpretability, or Efficiency?
- Interpretability
- Accuracy
- Scalability
- Efficiency
The concern when machine learning models make decisions without human understanding is primarily related to "Interpretability." A lack of interpretability can lead to mistrust and challenges in understanding why a model made a particular decision.
One of the drawbacks of using t-SNE is that it's not deterministic, meaning multiple runs with the same data can yield ________ results.
- Different
- Identical
- Similar
- Unpredictable
t-SNE (t-Distributed Stochastic Neighbor Embedding) is a probabilistic dimensionality reduction technique. Its non-deterministic nature means that each run may result in a different embedding, making the results unpredictable.
Which algorithm is commonly used for blind source separation or separating mixed signals?
- Principal Component Analysis (PCA)
- Support Vector Machine (SVM)
- K-Means Clustering
- Decision Trees
Principal Component Analysis (PCA) is commonly used for blind source separation, reducing the dimensionality of data to separate mixed signals. PCA identifies the principal components or directions of maximum variance in the data.
SVMs aim to maximize the margin, which is the distance between the decision boundary and the nearest ______ from any class.
- Decision Tree
- Hyperplane
- Outlier
- Support Vector
SVMs aim to maximize the margin, which is the distance between the decision boundary and the nearest support vector from any class. Support vectors play a crucial role in defining the decision boundary.
The equation y=mx+cy=mx+c is a simple representation of ________ regression.
- Linear
- Logistic
- Polynomial
- Ridge
The equation y=mx+c represents a simple linear regression. In this equation, 'y' is the dependent variable, 'x' is the independent variable, 'm' is the slope, and 'c' is the intercept. It's used to model a linear relationship between variables.
You are working on a fraud detection system where false negatives (failing to detect a fraud) can have severe financial implications. Which metric would you prioritize to ensure that as many actual fraud cases as possible are detected?
- Accuracy
- F1 Score
- Precision
- Recall
In this high-stakes scenario, prioritizing Recall is crucial. Recall measures the ability to detect actual fraud cases, minimizing false negatives, which is of paramount importance in a fraud detection system with severe financial consequences.
In GANs, what is the significance of the Nash Equilibrium?
- It's a point where both the generator and discriminator are optimal.
- It's a theoretical concept without practical relevance.
- It's the point where only the generator is optimal.
- It's the point where only the discriminator is optimal.
The Nash Equilibrium in GANs is when both the generator and discriminator reach an optimal state. It signifies stability in GAN training.
Why might one opt to use a Deep Q Network over traditional Q-learning for certain problems?
- Better handling of high-dimensional input data
- Faster convergence
- More efficient memory usage
- Enhanced exploration capabilities
Deep Q Networks (DQNs) are capable of handling high-dimensional input data, making them suitable for complex problems, unlike traditional Q-learning.
In a scenario with a high cost of false positives, one might prioritize a high ________ score.
- Precision
- Recall
- Sensitivity
- Specificity
In a scenario with a high cost of false positives, one should prioritize a high Precision score. Precision focuses on minimizing false positives, making it crucial when there's a high cost associated with making incorrect positive predictions. Sensitivity (Recall) is more focused on minimizing false negatives. Specificity is related to true negatives.
Imagine a scenario where multiple instruments play simultaneously, and you want to isolate the sound of each instrument. Which algorithm would be most appropriate for this task?
- Independent Component Analysis
- Principal Component Analysis
- k-Means Clustering
- Decision Trees
Independent Component Analysis (ICA) is a suitable technique for sound source separation. It can disentangle mixed sound signals into their original sources.