In the context of machine learning, what is the main difference between supervised and unsupervised learning in terms of data?
- Feature selection
- Hyperparameter tuning
- Labeled data
- Unlabeled data
The main difference between supervised and unsupervised learning is the presence of labeled data in supervised learning. In supervised learning, the model is trained using labeled data, which means it knows the correct answers. Unsupervised learning, on the other hand, works with unlabeled data, where the model has to find patterns and relationships on its own. Feature selection and hyperparameter tuning are aspects of model training but not the key distinction.
Which component of the Actor-Critic model is responsible for evaluating the actions taken by the agent?
- The Critic
- The Actor
- The Decision Maker
- The Environment
The Critic in the Actor-Critic architecture evaluates the actions taken by the agent by providing feedback on the quality of these actions.
An advanced application of NLP in healthcare is the creation of virtual health assistants or ________.
- Chatbots
- Recipe Generators
- Weather Predictors
- Gaming Characters
NLP in healthcare can create virtual health assistants, known as chatbots, to assist with medical inquiries and provide information to patients.
Consider a self-driving car learning from trial and error in a simulated environment. This is an example of which type of learning?
- Deep Learning
- Reinforcement Learning
- Supervised Learning
- Unsupervised Learning
This scenario exemplifies Reinforcement Learning. In Reinforcement Learning, an agent learns to take actions in an environment to maximize a reward signal. The self-driving car explores different actions (e.g., steering, accelerating, braking) and learns from the consequences in a simulated environment to improve its driving skills.
________ is the problem when a model learns the training data too well, including its noise and outliers.
- Bias
- Overfitting
- Underfitting
- Variance
Overfitting is the problem where a model becomes too specialized in the training data and captures its noise and outliers. This can lead to poor performance on unseen data.
When dealing with a small dataset and wanting to leverage the knowledge from a model trained on a larger dataset, which approach would be most suitable?
- Fine-tuning
- Transfer Learning
- Random Initialization
- Gradient Descent Optimization
The most suitable approach for leveraging knowledge from a model trained on a larger dataset with a small dataset is "Transfer Learning." It involves adapting the pre-trained model to the new task.
In hierarchical clustering, as the name suggests, the data is grouped into a hierarchy of clusters. What visualization is commonly used to represent this hierarchy?
- Bar Chart
- Dendrogram
- Heatmap
- Scatter Plot
A dendrogram is commonly used in hierarchical clustering to visualize the hierarchical structure of clusters, showing the merging and splitting of clusters.
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.
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.