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.
For binary classification tasks, which regression outputs a probability score between 0 and 1?
- Lasso Regression
- Linear Regression
- Logistic Regression
- Support Vector Regression
Logistic Regression outputs probability scores between 0 and 1, making it suitable for binary classification. It uses the logistic function to model the probability of the positive class.
If you want to visualize high-dimensional data in a 2D or 3D space, which of the following techniques would be suitable?
- Principal Component Analysis
- Regression Analysis
- Naive Bayes
- Linear Discriminant Analysis
Principal Component Analysis (PCA) is suitable for visualizing high-dimensional data in a lower-dimensional space. It identifies the directions of maximum variance, making data more manageable for visualization.
When an agent overly focuses on actions that have previously yielded rewards without exploring new possibilities, it might fall into a ________ trap.
- Exploitation
- Exploration
- Learning
- Reward
If an agent overly focuses on actions that have yielded rewards in the past, it falls into an exploitation trap, neglecting the exploration needed to find potentially better actions.
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.