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.
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.
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.
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.
You have a dataset with numerous features, and you suspect that many of them are correlated. Using which technique can you both reduce the dimensionality and tackle multicollinearity?
- Data Imputation
- Decision Trees
- Feature Scaling
- Principal Component Analysis (PCA)
Principal Component Analysis (PCA) can reduce dimensionality by transforming correlated features into a smaller set of uncorrelated variables. It addresses multicollinearity by creating new axes (principal components) where the original variables are no longer correlated, thus improving the model's stability and interpretability.
Why might it be problematic if a loan approval machine learning model is not transparent and explainable in its decision-making process?
- Increased risk of discrimination
- Enhanced privacy protection
- Improved loan approval process
- Faster decision-making
If a loan approval model is not transparent and explainable, it may lead to increased risks of discrimination, as it becomes unclear why certain applicants were approved or denied loans, potentially violating anti-discrimination laws.
Which regularization technique adds a penalty equivalent to the absolute value of the magnitude of coefficients?
- Elastic Net
- L1 Regularization
- L2 Regularization
- Ridge Regularization
L1 Regularization, also known as Lasso, adds a penalty equivalent to the absolute value of coefficients. This helps in feature selection by encouraging some coefficients to become exactly zero.
An autoencoder's primary objective is to minimize the difference between the input and the ________.
- Output
- Reconstruction
- Encoding
- Activation
The primary objective of an autoencoder is to minimize the difference between the input and its 'Reconstruction,' which is the encoded-decoded output.
A ________ is a tool in machine learning that helps...
- Feature Extractor
- Principal Component Analysis (PCA)
- Gradient Descent
- Overfitting
Principal Component Analysis (PCA) is a technique used for dimensionality reduction. It identifies and retains important information while reducing the number of input variables in a dataset.
In binary classification, if a model correctly predicts all positive instances and no negative instances as positive, its ________ will be 1.
- Accuracy
- F1 Score
- Precision
- Recall
When a model correctly predicts all positive instances and no negative instances as positive, it means it has perfect "precision." Precision measures how many of the predicted positive instances were correct.
One of the drawbacks of using t-SNE is that it's not deterministic, meaning multiple runs with the same data can yield ________ results.
- Different
- Identical
- Similar
- Unpredictable
t-SNE (t-Distributed Stochastic Neighbor Embedding) is a probabilistic dimensionality reduction technique. Its non-deterministic nature means that each run may result in a different embedding, making the results unpredictable.
Which of the following is a concern when machine learning models make decisions without human understanding: Accuracy, Scalability, Interpretability, or Efficiency?
- Interpretability
- Accuracy
- Scalability
- Efficiency
The concern when machine learning models make decisions without human understanding is primarily related to "Interpretability." A lack of interpretability can lead to mistrust and challenges in understanding why a model made a particular decision.