Which classifier is based on applying Bayes' theorem with the assumption of independence between every pair of features?
- K-Means
- Naive Bayes
- Random Forest
- Support Vector Machine
Naive Bayes is a classifier based on Bayes' theorem with the assumption of feature independence, making it effective for text classification.
Which type of regression is used to predict the probability of a categorical outcome?
- Decision Tree Regression
- Linear Regression
- Logistic Regression
- Polynomial Regression
Logistic Regression is specifically designed for predicting the probability of a categorical outcome. It's used when the dependent variable is binary (e.g., spam or not spam).
How can biases in training data affect the fairness of a machine learning model?
- Bias in training data can lead to underrepresented groups not being considered
- Bias can lead to faster training
- Bias has no impact on model fairness
- Bias can improve model fairness
Biases in training data can lead to underrepresentation of certain groups, causing the model to make unfair predictions, especially for those underrepresented groups.
Sparse autoencoders enforce a sparsity constraint on the activations of the ________ to ensure that only a subset of neurons are active at a given time.
- Hidden Layer
- Output Layer
- Input Layer
- Activation Function
Sparse autoencoders typically enforce a sparsity constraint on the activations of the hidden layer. This constraint encourages only a subset of neurons to be active at a given time, which can help in feature learning and dimensionality reduction.
Consider a scenario where a drone is learning to navigate through a maze. Which reinforcement learning algorithm can be utilized to train the drone?
- Q-Learning
- A* Search
- Breadth-First Search
- Genetic Algorithm
Q-Learning is a reinforcement learning algorithm suitable for training the drone. It allows the drone to learn through exploration and exploitation, optimizing its path in the maze while considering rewards and penalties.
In the realm of healthcare, how can machine learning and NLP together assist in the early detection of diseases?
- Analyzing Unstructured Clinical Text
- Image Analysis for Diagnosis
- Patient Demographics and Billing Data Analysis
- Genetic Testing Data Analysis
Machine learning and NLP can assist in early disease detection by analyzing unstructured clinical text, such as doctors' notes and patient records, to identify symptoms and risk factors. This goes beyond structured data analysis and helps in diagnosing diseases at an earlier stage.
Imagine a scenario where multiple instruments play simultaneously, and you want to isolate the sound of each instrument. Which algorithm would be most appropriate for this task?
- Independent Component Analysis
- Principal Component Analysis
- k-Means Clustering
- Decision Trees
Independent Component Analysis (ICA) is a suitable technique for sound source separation. It can disentangle mixed sound signals into their original sources.
In a scenario with a high cost of false positives, one might prioritize a high ________ score.
- Precision
- Recall
- Sensitivity
- Specificity
In a scenario with a high cost of false positives, one should prioritize a high Precision score. Precision focuses on minimizing false positives, making it crucial when there's a high cost associated with making incorrect positive predictions. Sensitivity (Recall) is more focused on minimizing false negatives. Specificity is related to true negatives.
Why might one opt to use a Deep Q Network over traditional Q-learning for certain problems?
- Better handling of high-dimensional input data
- Faster convergence
- More efficient memory usage
- Enhanced exploration capabilities
Deep Q Networks (DQNs) are capable of handling high-dimensional input data, making them suitable for complex problems, unlike traditional Q-learning.
In GANs, what is the significance of the Nash Equilibrium?
- It's a point where both the generator and discriminator are optimal.
- It's a theoretical concept without practical relevance.
- It's the point where only the generator is optimal.
- It's the point where only the discriminator is optimal.
The Nash Equilibrium in GANs is when both the generator and discriminator reach an optimal state. It signifies stability in GAN training.
You are working on a fraud detection system where false negatives (failing to detect a fraud) can have severe financial implications. Which metric would you prioritize to ensure that as many actual fraud cases as possible are detected?
- Accuracy
- F1 Score
- Precision
- Recall
In this high-stakes scenario, prioritizing Recall is crucial. Recall measures the ability to detect actual fraud cases, minimizing false negatives, which is of paramount importance in a fraud detection system with severe financial consequences.
The equation y=mx+cy=mx+c is a simple representation of ________ regression.
- Linear
- Logistic
- Polynomial
- Ridge
The equation y=mx+c represents a simple linear regression. In this equation, 'y' is the dependent variable, 'x' is the independent variable, 'm' is the slope, and 'c' is the intercept. It's used to model a linear relationship between variables.