n the context of CNNs, why are pooling layers important despite them leading to a loss of information?
- Pooling layers help reduce the spatial dimensions, aiding in computation
- Pooling layers introduce non-linearity and increase model complexity
- Pooling layers reduce the number of filters in the network
- Pooling layers improve interpretability of features
Pooling layers are crucial for dimensionality reduction, making computations feasible, and for creating translation-invariant features. Despite information loss, it retains the most essential features.
In K-means clustering, the value of K represents the number of ________.
- Clusters
- Data Points
- Features
- Centroids
In K-means clustering, 'K' represents the number of clusters you want to partition your data into. Each cluster will have its centroid.
In a DQN, the primary function of the neural network is to approximate which function?
- State-Action Value Function
- Policy Function
- Environment Dynamics Function
- Reward Function
The primary role of the neural network in a Deep Q Network (DQN) is to approximate the State-Action Value Function (Q-function).
You are working on a dataset with a large number of features. While some of them seem relevant, many appear to be redundant or irrelevant. What technique would you employ to enhance model performance and interpretability?
- Data Normalization
- Feature Scaling
- Principal Component Analysis (PCA)
- Recursive Feature Elimination (RFE)
Principal Component Analysis (PCA) is a dimensionality reduction technique that can help reduce the number of features while preserving the most important information. It enhances model performance by eliminating redundant features and improves interpretability by transforming the data into a new set of uncorrelated variables.
Which term describes a model that has been trained too closely to the training data and may not perform well on new, unseen data?
- Bias
- Generalization
- Overfitting
- Underfitting
Overfitting is a common issue in machine learning where a model becomes too specialized to the training data and fails to generalize well to new data. It's essential to strike a balance between fitting the training data and generalizing to unseen data.
Which RNN architecture is more computationally efficient but might not capture all the intricate patterns that its counterpart can: LSTM or GRU?
- GRU
- LSTM
- Both capture patterns efficiently
- Neither captures patterns effectively
The GRU (Gated Recurrent Unit) is more computationally efficient than LSTM (Long Short-Term Memory) but may not capture all intricate patterns in data due to its simplified architecture. LSTM is more expressive but computationally demanding.
In the context of machine learning, what is the primary concern of fairness?
- Bias
- Overfitting
- Underfitting
- Feature Selection
The primary concern in fairness within machine learning is 'Bias.' Bias can lead to unequal treatment or discrimination, especially when making predictions in sensitive areas like lending or hiring.
In ________ learning, algorithms are trained on labeled data, where the answer key is provided.
- Reinforcement
- Semi-supervised
- Supervised
- Unsupervised
In supervised learning, algorithms are trained using labeled data, which means each input is associated with the correct answer. This helps the algorithm learn and make predictions or classifications.
Which term refers to the error introduced by the tendency of a model to fit the training data too closely, capturing noise?
- Bias
- Overfitting
- Underfitting
- Variance
Overfitting is the term used to describe when a model fits the training data too closely, capturing noise and leading to poor generalization on unseen data. It results in a high variance.
When a machine learning algorithm tries to group data into clusters without prior labels, it is using which type of learning?
- Reinforcement Learning
- Semi-Supervised Learning
- Supervised Learning
- Unsupervised Learning
Unsupervised Learning is the technique where the algorithm groups data into clusters without prior labels. This can help identify patterns and relationships within the data.