A common measure of performance in the multi-armed bandit problem is the cumulative ________ over time.
- Rewards
- Q-values
- States
- Actions
The cumulative rewards over time are a common measure of performance in the multi-armed bandit problem, as you aim to maximize total reward.
When a model has very high variance and is too complex, which problem is it likely facing?
- Bias
- Noise
- Overfitting
- Underfitting
When a model has high variance and complexity, it is likely facing overfitting. Overfit models perform well on training data but poorly on new, unseen data, as they've learned to capture noise in the data, not the underlying patterns.
When a machine learning algorithm tries to group...
- Reinforcement Learning
- Semi-Supervised Learning
- Supervised Learning
- Unsupervised Learning
Unsupervised learning involves clustering or grouping data without prior labels. Algorithms in this approach aim to identify patterns and structure in the data without any guidance from labeled examples.
An e-commerce company has collected data about user behavior on their website. They are now interested in segmenting their users based on similar behaviors to provide personalized recommendations. While they considered decision trees, they were concerned about stability and overfitting. Which ensemble method might they consider as an alternative?
- AdaBoost
- Bagging (Bootstrap Aggregating)
- Gradient Boosting
- XGBoost
Gradient Boosting is a strong alternative. It's an ensemble method that combines the predictions of multiple decision trees, focusing on correcting the errors of previous trees. It typically performs well, provides stability, and mitigates overfitting concerns.
The value at which the sigmoid function outputs a 0.5 probability, thereby determining the decision boundary in logistic regression, is known as the ________.
- Decision Point
- Inflection Point
- Sigmoid Threshold
- Threshold Value
The value at which the sigmoid function outputs a 0.5 probability is known as the decision point. This is the threshold value that separates the two classes in a binary logistic regression.
In which learning approach does the model learn to...
- Reinforcement Learning
- Semi-Supervised Learning
- Supervised Learning
- Unsupervised Learning
In reinforcement learning, a model learns by interacting with an environment and receiving rewards or penalties based on its actions. It aims to make decisions to maximize cumulative rewards.
What is the primary reason for using Random Forests over a single Decision Tree in many applications?
- Faster training time
- Increased accuracy
- Lower memory usage
- Simplicity
Random Forests are preferred due to their increased accuracy over single Decision Trees. They work by aggregating the predictions of multiple trees, which reduces overfitting and results in better overall performance.
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).