In logistic regression, the log odds of the dependent variable is modeled as a linear combination of the independent variables using the ________ function.
- Hypothesis
- Logit
- Probability
- Sigmoid
In logistic regression, the log odds of the dependent variable is modeled using the Logit function. The Logit function is the inverse of the sigmoid function and is used to map linear combinations of independent variables to the range of real numbers.
Using domain knowledge to transform raw data into a more suitable format or variable for modeling is known as ________.
- Data Preprocessing
- Data Transformation
- Feature Engineering
- Raw Data Analysis
The process of using domain knowledge to transform raw data into a more suitable format or create new variables is called "feature engineering." It is a critical step in data preparation to improve model performance by providing more informative features.
A research institution is working on a system to automatically summarize long medical research papers into concise abstracts. This task primarily requires expertise in which domain?
- Medical Domain
- Computer Science
- Linguistics
- Information Retrieval
Summarizing research papers into concise abstracts relies heavily on linguistic expertise to ensure the summaries are coherent and convey the key information effectively.
In the Actor-Critic architecture, which part directly decides on the action to be taken?
- The Actor
- The Critic
- The Decision Maker
- The Environment
The Actor is directly responsible for deciding on the action to be taken, while the Critic provides feedback on these actions.
The ________ classifier calculates the probability of a particular event based on prior knowledge of conditions related to that event.
- Decision Tree
- K-Means
- Naive Bayes
- Support Vector Machine
The Naive Bayes classifier calculates the probability of an event based on prior knowledge of conditions. It's a probabilistic classifier.
When determining the number of clusters (K) for K-means clustering, which method involves plotting the variance as K increases and looking for an "elbow" in the plot?
- Davies-Bouldin Index
- Elbow Method
- Gap Statistics
- Silhouette Analysis
The Elbow Method involves plotting the variance (inertia) against different values of K. The "elbow" point is where the rate of decrease sharply changes, indicating an optimal K.
The balance between taking known rewarding actions and discovering new ones is termed as the ________ dilemma.
- Exploration-Exploitation
- Action-Selection
- Reinforcement-Reward
- Decision-Making
This is known as the Exploration-Exploitation dilemma, which is central in Reinforcement Learning, referring to the balance between exploiting known actions for rewards and exploring new actions to discover more rewarding ones.
In a neural network, what are the nodes that receive input data and pass it forward called?
- Neurons
- Synapses
- Layers
- Weights
In a neural network, the nodes that receive input data and pass it forward are called "Neurons." Neurons process and transmit information.
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.