Before deploying a model into production in the Data Science Life Cycle, it's essential to have a _______ phase to test the model's real-world performance.
- Training phase
- Deployment phase
- Testing phase
- Validation phase
Before deploying a model into production, it's crucial to have a testing phase to evaluate the model's real-world performance. This phase assesses how the model performs on unseen data to ensure its reliability and effectiveness.
You're analyzing a dataset with the heights of individuals. While the mean height is 165 cm, you notice a few heights recorded as 500 cm. These values are likely:
- Data entry errors
- Outliers
- Missing data
- Measurement errors
The heights recorded as 500 cm are likely outliers in the dataset. Outliers are data points that significantly differ from the majority of the data and may indicate measurement errors or anomalies. It's important to identify and handle outliers appropriately during data analysis.
Which advanced technique in computer vision involves segmenting each pixel of an image into a specific class?
- Object detection
- Semantic segmentation
- Image classification
- Edge detection
Semantic segmentation is an advanced computer vision technique that involves classifying each pixel in an image into a specific class or category. It's used for tasks like identifying object boundaries and segmenting objects within an image.
In the context of neural networks, what is the role of a hidden layer?
- It stores the input data
- It performs the final prediction
- It extracts and transforms features
- It provides feedback to the user
The role of a hidden layer in a neural network is to extract and transform features from the input data. Hidden layers learn to represent the data in a way that makes it easier for the network to make predictions or classifications. They are essential for capturing the underlying patterns and relationships in the data.
Among Data Engineer, Data Scientist, and Data Analyst, who is more likely to be proficient in advanced statistical modeling?
- Data Engineer
- Data Scientist
- Data Analyst
- All of the above
Data Scientists are typically proficient in advanced statistical modeling. They use statistical techniques to analyze data and create predictive models. While Data Analysts may also have statistical skills, Data Scientists specialize in this area.
Ensemble methods like Random Forest and Gradient Boosting work by combining multiple _______ to improve overall performance.
- Features
- Models
- Datasets
- Metrics
Ensemble methods, like Random Forest and Gradient Boosting, combine multiple models (decision trees in the case of Random Forest) to improve overall predictive performance. These models are trained independently and then aggregated to make predictions. The combination of models is what enhances the accuracy and robustness of the ensemble.
The process of transforming skewed data into a more Gaussian-like distribution is known as _______.
- Normalization
- Standardization
- Imputation
- Resampling
The process of transforming skewed data into a more Gaussian-like distribution is called "standardization." It involves shifting the data's distribution to have a mean of 0 and a standard deviation of 1, making it more amenable to certain statistical techniques.
Which method involves filling missing values in a dataset using the column's average?
- Min-Max Scaling
- Imputation with Mean
- Standardization
- Principal Component Analysis
Imputation with Mean is a common technique in Data Science to fill missing values by replacing them with the mean of the respective column. It helps maintain the integrity of the dataset by using the column's central tendency.
In the context of data warehousing, which process is responsible for periodically loading fresh data into the data warehouse?
- Data Extraction
- Data Transformation
- Data Loading
- Data Integration
Data Loading is the process responsible for periodically loading fresh data into the data warehouse. It involves taking the data extracted from source systems, transforming it into the appropriate format, and then loading it into the data warehouse for analysis and reporting. Data Extraction, Transformation, and Integration are important steps in this process but are not solely responsible for loading data into the warehouse.
What is the primary purpose of using activation functions in neural networks?
- To add complexity to the model
- To control the learning rate
- To introduce non-linearity in the model
- To speed up the training process
The primary purpose of activation functions in neural networks is to introduce non-linearity into the model. Without non-linearity, neural networks would reduce to linear regression models, limiting their ability to learn complex patterns in data. Activation functions enable neural networks to approximate complex functions and make them suitable for a wide range of tasks.