If you're working with high-dimensional data and you want to reduce its dimensionality for visualization without necessarily preserving the global structure, which method would be apt?
- Principal Component Analysis (PCA)
- Linear Discriminant Analysis (LDA)
- t-Distributed Stochastic Neighbor Embedding (t-SNE)
- Independent Component Analysis (ICA)
When you want to reduce high-dimensional data for visualization without preserving global structure, t-SNE is apt. It focuses on local similarities, making it effective for revealing clusters and patterns in the data, even if the global structure is not preserved.
In the context of healthcare, what is the significance of machine learning models being interpretable?
- To provide insights into the model's decision-making process and enable trust in medical applications
- To speed up the model training process
- To make models run on low-end hardware
- To reduce the amount of data required
Interpretable models are essential in healthcare to ensure that the decisions made by the model are understandable and can be trusted, which is crucial for patient safety and regulatory compliance.
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.
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.
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.
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.
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.
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.
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.