Which type of learning would be best suited for categorizing news articles into topics without pre-defined categories?
- Reinforcement learning
- Semi-supervised learning
- Supervised learning
- Unsupervised learning
Unsupervised learning is the best choice for categorizing news articles into topics without predefined categories. Unsupervised learning algorithms can cluster similar articles based on patterns and topics discovered from the data without the need for labeled examples. Reinforcement learning is more suitable for scenarios with rewards and actions. Supervised learning requires labeled data, and semi-supervised learning combines labeled and unlabeled data.
In SVM, what does the term "kernel" refer to?
- A feature transformation
- A hardware component
- A software component
- A support vector
The term "kernel" in Support Vector Machines (SVM) refers to a feature transformation. Kernels are used to map data into a higher-dimensional space, making it easier to find a linear hyperplane that separates different classes.
The Naive Bayes classifier assumes that the presence or absence of a particular feature of a class is ________ of the presence or absence of any other feature.
- Correlated
- Dependent
- Independent
- Unrelated
Naive Bayes assumes that features are independent of each other. This simplifying assumption helps make the algorithm computationally tractable but might not hold in all real-world cases.
Regularization techniques help in preventing overfitting. Which of these is NOT a regularization technique: Batch Normalization, Dropout, Adam Optimizer, L1 Regularization?
- Adam Optimizer
- Batch Normalization
- Dropout
- L1 Regularization
Adam Optimizer is not a regularization technique. It's an optimization algorithm used in training neural networks, while the others are regularization methods.
A medical research team is studying the relationship between various health metrics (like blood pressure, cholesterol level) and the likelihood of developing a certain disease. The outcome is binary (disease: yes/no). Which regression model should they employ?
- Decision Tree Regression
- Linear Regression
- Logistic Regression
- Polynomial Regression
Logistic Regression is the appropriate choice for binary outcomes, such as the likelihood of developing a disease (yes/no). It models the probability of a binary outcome based on predictor variables, making it well-suited for this medical research.
In the context of the multi-armed bandit problem, what is regret?
- The feeling of loss and remorse
- An optimization metric
- A random variable
- An arm selection policy
In the context of the multi-armed bandit problem, regret is an optimization metric that quantifies how much an agent's total reward falls short of the best possible reward it could have achieved by always choosing the best arm. It's a way to measure how well an agent's arm selection policy performs.
When NLP systems try to understand the context of words in medical documents to extract meaningful information, they are leveraging a technique called ________.
- Named Entity Recognition
- Sentiment Analysis
- Document Summarization
- Word Embeddings
Named Entity Recognition is a technique in NLP used to identify and classify entities in medical documents, such as drugs, diseases, or patient names.
In which scenario is unsupervised learning least suitable: predicting house prices based on features, grouping customers into segments, or classifying emails as spam or not spam?
- Classifying emails as spam or not spam
- Grouping customers into segments
- Predicting house prices based on features
- Unsupervised learning is suitable for all scenarios
Unsupervised learning is least suitable for classifying emails as spam or not spam. This is because unsupervised learning doesn't have labeled data to distinguish between spam and non-spam emails. It is more applicable to clustering or grouping data when you don't have clear labels, such as grouping customers into segments.
While supervised learning requires explicit labels, ________ learning operates on data without explicit instructions.
- Deep
- Reinforcement
- Semi-supervised
- Unsupervised
In machine learning, unsupervised learning operates on data without explicit labels or instructions. Supervised learning, on the other hand, relies heavily on labeled data.
CNNs are particularly effective for image data due to their ability to preserve the ________ structure of the data.
- Spatial
- Color
- Temporal
- Frequency
CNNs are effective for image data due to their ability to preserve the spatial structure of the data, which is crucial for detecting patterns in pixels' proximity.