In the context of text classification, Naive Bayes often works well because it can handle what type of data?
- High-Dimensional and Sparse Data
- Images and Videos
- Low-Dimensional and Dense Data
- Numeric Data
Naive Bayes is effective with high-dimensional and sparse data as it assumes independence between features, making it suitable for text data with numerous attributes.
In CNNs, the ________ layer is responsible for detecting features in an image.
- Convolutional
- Pooling
- Fully Connected
- Input
The 'Convolutional' layer in a Convolutional Neural Network (CNN) is responsible for detecting features in an image using convolution operations.
Which ensemble method combines multiple decision trees and aggregates their results for improved accuracy and reduced overfitting?
- Logistic Regression
- Naive Bayes
- Principal Component Analysis (PCA)
- Random Forest
Random Forest is an ensemble method that combines multiple decision trees. It aggregates their results through techniques like bagging and boosting to achieve better accuracy and reduce overfitting. Random Forest is a popular choice for various machine learning tasks.
Imagine you're developing a model to recognize rare bird species from images. You don't have many labeled examples of these rare birds, but you have a model trained on thousands of common bird species. How might you leverage this existing model for your task?
- Fine-tuning the Pre-trained Model
- Random Initialization of Weights
- Training the Model from Scratch
- Using the Model Only for Common Bird Recognition
Fine-tuning involves taking a pre-trained model and adjusting its parameters, typically only in the final layers, to specialize it for your specific task, which is recognizing rare bird species in this case.
Which clustering method assigns data points to the nearest cluster center and recalculates the center until convergence?
- Agglomerative
- DBSCAN
- Hierarchical
- K-Means
K-Means clustering is an iterative algorithm that assigns each data point to the nearest cluster center, recalculating these centers until they converge.
t-SNE is particularly known for preserving which kind of structures from the high-dimensional data in the low-dimensional representation?
- Global Structures
- Local Structures
- Numerical Structures
- Geometric Structures
t-SNE is known for preserving local structures in the low-dimensional representation, making it effective for visualization and capturing fine-grained relationships.
When both precision and recall are important for a problem, one might consider optimizing the ________ score.
- Accuracy
- F1 Score
- ROC AUC
- Specificity
The F1 Score is a measure that balances both precision and recall. It is especially useful when you want to consider both false positives and false negatives in your classification problem.
When using K-means clustering, why is it sometimes recommended to run the algorithm multiple times with different initializations?
- To ensure deterministic results.
- To make the algorithm run faster.
- To mitigate sensitivity to initial cluster centers.
- To reduce the number of clusters.
K-means clustering is sensitive to initial cluster centers. Running it multiple times with different initializations helps find a more stable solution.
In the context of machine learning, what is a time series?
- A series of chronological events
- A list of sorted numbers
- A collection of images
- A data structure
A time series is a series of chronological events or data points collected or recorded at equally spaced time intervals. It's commonly used for forecasting and analysis.
You're working on a project where you need to predict the next word in a sentence based on the previous words. Which type of neural network architecture would be most appropriate for this task?
- Recurrent Neural Network (RNN)
- Convolutional Neural Network (CNN)
- Long Short-Term Memory (LSTM)
- Gated Recurrent Unit (GRU)
A Long Short-Term Memory (LSTM) is well-suited for this task because it can capture long-term dependencies in sequential data, making it effective for predicting the next word based on previous words in a sentence.