How do Policy Gradient Methods differ from value-based methods in their approach to reinforcement learning?
- Value-based methods learn
- They learn both the
- Policy Gradient Methods
- They learn neither
Policy Gradient Methods focus on learning the policy directly, which means they determine the probability of taking actions. Value-based methods, on the other hand, learn the value of states or state-action pairs. This key difference is essential for understanding the approach to RL each method takes.
In the context of text classification, Naive Bayes often works well because it can handle what type of data?
- Categorical Data
- High-Dimensional Data
- Numerical Data
- Time Series Data
Naive Bayes works well in text classification because it can effectively handle high-dimensional data with numerous features (words or terms).
How do residuals, the differences between the observed and predicted values, relate to linear regression?
- They are not relevant in linear regression
- They indicate how well the model fits the data
- They measure the strength of the relationship between predictors
- They represent the sum of squared errors
Residuals in linear regression measure how well the model fits the data. Specifically, they represent the differences between the observed and predicted values. Smaller residuals indicate a better fit, while larger residuals suggest a poorer fit.
In a case where a company wants to detect abnormal patterns in vast amounts of transaction data, which type of neural network model would be particularly beneficial in identifying these anomalies based on data reconstructions?
- Variational Autoencoder
- Long Short-Term Memory (LSTM)
- Feedforward Neural Network
- Restricted Boltzmann Machine
Variational Autoencoders (VAEs) are excellent for anomaly detection because they model data distributions and can recognize deviations from these distributions.
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 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.
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.
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.