A company wants to deploy a machine learning model for hiring. They've ensured that the model is highly accurate. However, they're facing criticism because the inner workings of their model are a "black box," and candidates want to know why they were or were not selected. This criticism mainly pertains to which aspect of machine learning?
- Explainability
- Accuracy
- Training Data
- Hyperparameter Tuning
The criticism about the model being a "black box" highlights the need for explainability in machine learning. It's essential to understand how and why the model made hiring decisions, not just the accuracy of those decisions.
What challenges are typically faced when using traditional machine learning algorithms for time series forecasting, and how do modern techniques address them?
- Challenges: Lack of capturing complex patterns, limited feature engineering. Modern techniques employ deep learning models, recurrent neural networks (RNNs), and attention mechanisms to better capture patterns and require less manual feature engineering.
- Challenges: Modern techniques use the same principles as traditional algorithms but with faster computation.
- Challenges: Traditional algorithms are perfect for time series forecasting.
- Challenges: Modern techniques use ensemble learning.
Traditional algorithms often struggle to capture complex patterns in time series data and require extensive feature engineering. Modern techniques leverage deep learning, RNNs, and attention mechanisms to automatically capture complex patterns, reducing the need for manual feature engineering.
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 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.