A model that makes decisions without being able to provide clear reasoning behind them lacks ________.
- Transparency
- Performance
- Speed
- Scalability
Transparency in a model is the ability to explain its decision-making, which is crucial for trust, auditing, and regulatory compliance.
A hospital is trying to reduce the readmission rates of patients. They decide to use historical patient data, including treatment details, doctor's notes, and patient feedback. Which machine learning approach in healthcare would be most suitable for this?
- Natural Language Processing (NLP)
- Supervised Learning
- Reinforcement Learning
- Unsupervised Learning
Natural Language Processing (NLP) is the most suitable approach for extracting insights from textual data like doctor's notes and patient feedback, which can help in reducing readmission rates.
What is the primary challenge addressed by the multi-armed bandit problem?
- Balancing Exploration and Exploitation
- Image Recognition
- Language Translation
- Voice Assistant Development
The primary challenge of the multi-armed bandit problem is to balance Exploration (trying new actions) and Exploitation (choosing known good actions) to maximize cumulative rewards in a limited time.
In logistic regression, the log odds of the dependent variable is modeled as a linear combination of the independent variables using the ________ function.
- Hypothesis
- Logit
- Probability
- Sigmoid
In logistic regression, the log odds of the dependent variable is modeled using the Logit function. The Logit function is the inverse of the sigmoid function and is used to map linear combinations of independent variables to the range of real numbers.
A model that consistently predicts the same output regardless of the input data is said to have high ________.
- Accuracy
- Consistency
- Precision
- Variability
When a model consistently predicts the same output, it is considered to have high "consistency." This means it's not providing useful or varied predictions, which can be a problem in machine learning.
Hierarchical clustering that starts with each data point as its own cluster and gradually merges them is termed as ________ hierarchical clustering.
- Agglomerative
- Divisive
- Exclusive
- Partitioning
Agglomerative hierarchical clustering is the method where individual data points begin as clusters and then merge step by step based on similarity until they form a single cluster.
Which neural network architecture is specifically designed to work with sequences of data, such as time series or natural language?
- Convolutional Neural Network (CNN)
- Recurrent Neural Network (RNN)
- Long Short-Term Memory (LSTM)
- Gated Recurrent Unit (GRU)
Recurrent Neural Networks (RNNs) are designed for sequential data. They maintain hidden states that capture sequential dependencies, making them suitable for time series and language modeling.
A telemedicine platform wants to develop a feature where patients can describe their symptoms in natural language, and the system provides potential diagnoses. This feature would heavily rely on which technology?
- Natural Language Processing (NLP)
- Artificial Intelligence (AI)
- Blockchain Technology
- Augmented Reality (AR)
The feature of interpreting natural language symptoms and providing diagnoses relies on Natural Language Processing (NLP) to understand and process the patient's input effectively.
In a Gaussian Mixture Model, what parameter determines the shape, size, and orientation of the cluster?
- Covariance Matrix
- Number of Clusters
- Mean Vector
- Standard Deviation
In a Gaussian Mixture Model, the shape, size, and orientation of a cluster are determined by the covariance matrix, which represents the dispersion and correlation between data points in the cluster.
Using domain knowledge to transform raw data into a more suitable format or variable for modeling is known as ________.
- Data Preprocessing
- Data Transformation
- Feature Engineering
- Raw Data Analysis
The process of using domain knowledge to transform raw data into a more suitable format or create new variables is called "feature engineering." It is a critical step in data preparation to improve model performance by providing more informative features.