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.
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.
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).
In the Actor-Critic model, what role does the Critic's feedback play in adjusting the Actor's policies?
- Evaluating policy
- Selecting actions
- Providing rewards
- Discovering optimal actions
The Critic in the Actor-Critic model evaluates the current policy by estimating the value function. This evaluation helps the Actor make better decisions by guiding it towards actions that result in higher expected rewards, ultimately improving the policy.
An online retailer wants to recommend products to users. They have a vast inventory, and they're unsure which products are most likely to be purchased. Every time a product is recommended and purchased, the retailer gets a reward. This setup is reminiscent of which problem?
- Recommender Systems
- NLP for Sentiment Analysis
- Clustering and Dimensionality Reduction
- Reinforcement Learning
The retailer's challenge of recommending products and receiving rewards upon purchase aligns with Recommender Systems. In this problem, algorithms are used to predict user preferences and recommend items to maximize user satisfaction and sales.
If you want to predict whether an email is spam (1) or not spam (0), which regression technique would you use?
- Decision Tree Regression
- Linear Regression
- Logistic Regression
- Polynomial Regression
For this classification task (spam or not spam), Logistic Regression is appropriate. It models the probability of the email being spam and maps it to a binary outcome.
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.