For the k-NN algorithm, what could be a potential drawback of using a very large value of kk?
- Increased Model Bias
- Increased Model Variance
- Overfitting to Noise
- Slower Training Time
A potential drawback of using a large value of 'k' in k-NN is that it can overfit to noise in the data, leading to reduced accuracy on the test data.
Deep Q Networks (DQNs) are a combination of Q-learning and what other machine learning approach?
- Convolutional Neural Networks
- Recurrent Neural Networks
- Supervised Learning
- Unsupervised Learning
Deep Q Networks (DQNs) combine Q-learning with Convolutional Neural Networks (CNNs) to handle complex and high-dimensional state spaces.
What distinguishes autoencoders from other traditional neural networks in terms of their architecture?
- Autoencoders have an encoder and decoder
- Autoencoders use convolutional layers
- Autoencoders have more hidden layers
- Autoencoders don't use activation functions
Autoencoders have a distinct encoder-decoder architecture, enabling them to learn efficient representations of data and perform tasks like image denoising and compression.
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.
Why is feature selection important in building machine learning models?
- All of the Above
- Enhances Model Interpretability
- Reduces Overfitting
- Speeds up Training
Feature selection is important for various reasons. It reduces overfitting by focusing on relevant features, speeds up training by working with fewer features, and enhances model interpretability by highlighting the most important factors affecting predictions.
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.
Which type of machine learning is primarily concerned...
- Reinforcement Learning
- Semi-Supervised Learning
- Supervised Learning
- Unsupervised Learning
In supervised learning, the model is trained using labeled data, where input features are associated with known output labels. It learns to make predictions based on this labeled data.
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.
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).
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.