For the k-NN algorithm, what could be a potential drawback of using a very large value of k?
- Decreased Model Sensitivity
- Improved Generalization
- Increased Computational Cost
- Reduced Memory Usage
A large value of k in k-NN can make the model less sensitive to local patterns, leading to a loss in predictive accuracy due to averaging over more neighbors.
How does the architecture of a CNN ensure translational invariance?
- CNNs use weight sharing in convolutional layers, making features invariant to translation
- CNNs utilize pooling layers to reduce feature maps size
- CNNs randomly initialize weights to break translational invariance
- CNNs use a large number of layers for translation invariance
CNNs ensure translational invariance by sharing weights in convolutional layers, allowing learned features to detect patterns regardless of their location in the image. This is a key property of CNNs.
What does the "G" in GRU stand for when referring to a type of RNN?
- Gated
- Global
- Gradient
- Graph
The "G" in GRU stands for "Gated." GRU is a type of RNN that uses gating mechanisms to control information flow, making it capable of handling sequences efficiently.
One of the challenges in training deep RNNs is the ________ gradient problem, which affects the network's ability to learn long-range dependencies.
- Vanishing
- Exploding
- Overfitting
- Regularization
The vanishing gradient problem refers to the issue where gradients in deep RNNs become too small during training, making it challenging to capture long-range dependencies.
In the context of the bias-variance trade-off, which one is typically associated with complex models with many parameters?
- Balanced Bias-Variance
- High Bias
- High Variance
- Neither
High Variance is typically associated with complex models with many parameters. Complex models are more flexible and tend to fit the training data closely, resulting in high variance, which can lead to overfitting.
In time series forecasting, the goal is to predict future ________ based on past observations.
- Events
- Trends
- Weather
- Stock Prices
Time series forecasting aims to predict future trends or patterns based on historical data, which can be applied in various fields like finance or weather.
Decision Trees often suffer from ______, where they perform well on training data but poorly on new, unseen data.
- Overfitting
- Pruning
- Splitting
- Underfitting
Decision Trees are prone to "Overfitting," where they become too complex and fit the training data too closely. This can lead to poor generalization to new, unseen data.
Which of the following techniques is used to estimate future rewards in reinforcement learning?
- Q-Learning
- Gradient Descent
- Principal Component Analysis
- K-Means Clustering
Q-Learning is a technique in reinforcement learning used to estimate future rewards associated with taking actions in different states.
What is the potential consequence of deploying a non-interpretable machine learning model in a critical sector, such as medical diagnosis?
- Inability to explain decisions
- Improved accuracy
- Faster decision-making
- Better generalization
Deploying a non-interpretable model can result in a lack of transparency, making it challenging to understand how and why the model makes specific medical diagnosis decisions. This lack of transparency can be risky in critical sectors.
The ability of SVMs to handle non-linear decision boundaries is achieved by transforming the input data into a higher-dimensional space using a ______.
- Classifier
- Dimensionality Reduction
- Ensemble
- Kernel
SVMs use a mathematical function called a kernel to transform data into a higher-dimensional space, enabling them to handle non-linear decision boundaries effectively.