When a dataset has values ranging from 0 to 1000 in one column and 0 to 1 in another column, which transformation can be used to scale them to a similar range?
- Normalization
- Log Transformation
- Standardization
- Min-Max Scaling
Min-Max Scaling, also known as feature scaling, is used to transform values within a specific range (typically 0 to 1) for different features. It ensures that variables with different scales have a similar impact on the analysis.
For datasets with multiple features, EDA often involves dimensionality reduction techniques like PCA to visualize data in two or three _______.
- Planes
- Points
- Dimensions
- Directions
Exploratory Data Analysis (EDA) often employs dimensionality reduction techniques like Principal Component Analysis (PCA) to visualize data in lower-dimensional spaces (2 or 3 dimensions) for better understanding, hence the term "dimensions."
What is the primary goal of tokenization in NLP?
- Removing stop words
- Splitting text into words
- Extracting named entities
- Translating text to other languages
The primary goal of tokenization in NLP is to split text into words or tokens. This process is essential for various NLP tasks such as text analysis, language modeling, and information retrieval. Tokenization helps in breaking down text into meaningful units for analysis.
For models with a large number of layers, which technique helps in improving the internal covariate shift and accelerates the training?
- Stochastic Gradient Descent (SGD) with a small learning rate
- Batch Normalization
- L1 Regularization
- DropConnect
Batch Normalization is a technique used to improve the training of deep neural networks. It addresses the internal covariate shift problem by normalizing the activations of each layer. This helps in accelerating training and allows for the use of higher learning rates without the risk of divergence. It also aids in better gradient flow.
In the context of AI ethics, what is the primary concern of "interpretability"?
- Ensuring AI is always right
- Making AI faster
- Understanding how AI makes decisions
- Controlling the cost of AI deployment
"Interpretability" in AI ethics is about understanding how AI systems make decisions. It's crucial for accountability, transparency, and identifying and addressing potential biases in AI algorithms. AI being right or fast is important but not the primary concern in this context.
You are responsible for ensuring that the data in your company's data warehouse is consistent, reliable, and easily accessible. Recently, there have been complaints about data discrepancies. Which stage in the ETL process should you primarily focus on to resolve these issues?
- Extraction
- Transformation
- Loading
- Data Ingestion
The Transformation stage is where data discrepancies are often addressed. During transformation, data is cleaned, normalized, and validated to ensure consistency and reliability. This stage is critical for data quality and consistency in the data warehouse. Extraction involves collecting data, Loading is about data loading into the warehouse, and Data Ingestion is the process of bringing data into the system.
Which algorithm would you use when you have a mix of input features (both categorical and continuous) and you need to ensure interpretability of the model?
- Random Forest
- Support Vector Machines (SVM)
- Neural Networks
- Naive Bayes Classifier
Random Forest is a suitable choice for mixed input features when interpretability is important. It combines decision trees and is often used for feature selection and interpretability, making it a good option for such cases.
In a relational database, what is used to ensure data integrity across multiple tables?
- Primary Key
- Foreign Key
- Index
- Trigger
A Foreign Key is used in a relational database to ensure data integrity by creating a link between tables. It enforces referential integrity, ensuring that values in one table match values in another. Primary Keys are used to uniquely identify records in a table, not to maintain integrity across tables. Indexes and Triggers serve different purposes.
The _______ is a measure of the relationship between two variables and ranges between -1 and 1.
- P-value
- Correlation coefficient
- Standard error
- Regression
The measure of the relationship between two variables, ranging from -1 (perfect negative correlation) to 1 (perfect positive correlation), is known as the "correlation coefficient." It quantifies the strength and direction of the linear relationship between variables.
How do federated learning approaches differ from traditional machine learning in terms of data handling?
- Federated learning doesn't use data
- Federated learning relies on centralized data storage
- Federated learning trains models on decentralized data
- Traditional machine learning trains models on a single dataset
Federated learning trains machine learning models on decentralized data sources without transferring them to a central server. This approach is privacy-preserving and efficient. In contrast, traditional machine learning typically trains models on a single, centralized dataset, which may raise data privacy concerns.