If a classifier predicts the positive class perfectly but struggles with the negative class, the ________ might still be high due to the imbalance.
- AUC-ROC
- Accuracy
- F1 Score
- True Positive Rate
If a classifier predicts the positive class perfectly but struggles with the negative class, the Accuracy might still be high due to class imbalance. Accuracy can be misleading in imbalanced datasets because it doesn't account for the unequal distribution of classes. F1 Score and AUC-ROC are more robust metrics in such cases.
Which component of a GAN is responsible for generating new data samples?
- Discriminator
- Loss Function
- Optimizer
- Generator
The Generator component in a GAN is responsible for generating new data samples. It tries to create data indistinguishable from real data to deceive the Discriminator.
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.
What is a common application of GANs in the field of image processing?
- Image classification.
- Style transfer.
- Sentiment analysis.
- Speech recognition.
GANs are frequently used for style transfer, a technique that changes the artistic style of an image. It's commonly employed in fields like art and design for image manipulation and transformation.
If a machine learning model inadvertently learns societal biases present in its training data, it can result in ________ outcomes.
- Biased
- Fair
- Unpredictable
- Equitable
When a model learns societal biases, it can lead to biased outcomes, reinforcing existing prejudices and discrimination. It's crucial to address and mitigate such biases for more equitable results.
In the context of autoencoders, what is the significance of the "bottleneck" layer?
- The bottleneck layer reduces model complexity
- The bottleneck layer enhances training speed
- The bottleneck layer compresses input data
- The bottleneck layer adds noise to data
The "bottleneck" layer in an autoencoder serves as the compression layer, reducing input data to a lower-dimensional representation. This compression is essential for capturing essential features in a compact representation, facilitating feature extraction and denoising.
The ________ gate in an LSTM controls which parts of the cell state should be updated.
- Update
- Forget
- Input
- Output
In an LSTM (Long Short-Term Memory), the update gate (also known as the input gate) regulates which parts of the cell state should be updated based on the current input and previous state.
Which algorithm can be used for both regression and classification tasks, and is particularly well-suited for dealing with large data sets and high-dimensional spaces?
- Gradient Boosting
- K-Means
- Naive Bayes
- Random Forest
Gradient Boosting is an algorithm that can be used for both regression and classification tasks. It's known for its robustness in handling large datasets and high-dimensional spaces, making it a versatile choice.
One of the common algorithms used to solve the multi-armed bandit problem is the ________ algorithm.
- UCB (Upper Confidence Bound)
- Q-Learning
- A* (A-Star)
- K-Means
The Upper Confidence Bound (UCB) algorithm is a common approach to solving the multi-armed bandit problem, providing a balance between exploration and exploitation.