In clustering problems where the assumption is that...
- K-Means
- Gaussian Mixture Model (GMM)
- Support Vector Machines
- Decision Trees
Gaussian Mixture Model (GMM) is a popular choice in clustering problems where data is assumed to be generated from a mixture of Gaussian distributions. It can model complex data distributions effectively.
In which algorithm is the outcome determined based on a majority vote from its neighbors?
- K-Nearest Neighbors (K-NN)
- Linear Regression
- Logistic Regression
- Principal Component Analysis (PCA)
K-Nearest Neighbors (K-NN) is a classification algorithm where the outcome is determined by majority voting among its nearest neighbors.
When using transfer learning, what part of the pre-trained model is typically fine-tuned for the new task?
- Last few layers
- First few layers
- All layers
- Random layers
In transfer learning, the last few layers are typically fine-tuned because they contain task-specific information, while the early layers retain more generic features.
Which evaluation metric would be least affected by a large number of true negatives in a dataset?
- Accuracy
- Precision
- Recall
- Specificity
Specificity is the evaluation metric least affected by a large number of true negatives in a dataset. It focuses on correctly identifying true negatives and is particularly relevant in situations where false positives should be minimized.
A medical diagnosis AI system provides a diagnosis but does not give any rationale or reasoning behind it. What aspect of machine learning is this system lacking?
- Interpretability
- Classification
- Model Complexity
- Feature Engineering
The system's lack of providing rationale or reasoning is a deficiency in interpretability. In medical AI, it's crucial for doctors to understand why a diagnosis was made to trust and make informed decisions based on the AI's recommendations.
What is the primary objective of Generative Adversarial Networks (GANs)?
- Data Classification
- Data Generation
- Data Storage
- Data Analysis
The primary objective of GANs is data generation. GANs consist of a generator that creates data samples to closely resemble real data, aiding in tasks like image generation.
How does NLP handle the nuances and variations in medical terminologies across different healthcare systems?
- Named Entity Recognition and Mapping
- Machine Translation of Medical Terms
- Simple Text Matching
- Manual Reconciliation
NLP utilizes techniques like Named Entity Recognition to identify and map medical terms to a standardized vocabulary, addressing the nuances and variations in medical terminologies across different healthcare systems. This enables consistent analysis and retrieval of information.
A bank uses a machine learning model for loan approvals. However, it's observed that individuals from certain ethnic backgrounds are consistently getting rejected more than others, despite having similar financial profiles. This raises concerns related to which aspect of machine learning?
- Fairness & Bias
- Accuracy
- Data Preprocessing
- Model Training
This situation pertains to fairness and bias in machine learning, specifically, unfair outcomes or discrimination against certain ethnic groups, which highlights ethical concerns. It's important to address these biases for equitable decision-making.
The output of a GAN, after training, is a/an ________ that closely resembles the real data.
- Image
- Noise
- Anomaly
- Vector
The output of a GAN is typically an image, which is generated to closely resemble the real data it was trained on.
RNNs are particularly suitable for tasks like ________ because of their ability to handle sequences.
- Sentiment Analysis
- Image Classification
- Sequence Prediction
- Audio Recognition
RNNs excel in tasks that involve sequences, such as sequence prediction, where the previous elements influence the future ones.
In the multi-armed bandit problem, the challenge is to balance between exploration of arms and ________ of the best-known arm.
- Exploitation
- Reward accumulation
- Arm selection
- Probability estimation
The multi-armed bandit problem involves the trade-off between exploration (trying new arms) and exploitation (selecting the best-known arm).
Ensuring that a machine learning model does not unintentionally favor or discriminate against certain groups is ensuring its ________.
- Fairness
- Accuracy
- Efficiency
- Robustness
Ensuring fairness in machine learning models means preventing biases and discrimination in model predictions across different groups.