Unlike PCA, which assumes that the data components are orthogonally distributed, ICA assumes that the components are ________.
- Independent
- Correlated
- Uncorrelated
- Randomly Distributed
ICA (Independent Component Analysis) assumes that the components are independent of each other, not necessarily orthogonal, which is different from PCA. PCA assumes orthogonality, but ICA allows for any type of independence.
In which learning approach does the model learn to make decisions by receiving rewards or penalties for its actions?
- Reinforcement Learning
- Semi-Supervised Learning
- Supervised Learning
- Unsupervised Learning
Reinforcement Learning involves learning through trial and error. A model learns to make decisions by receiving rewards for good actions and penalties for bad ones. It's commonly used in areas like game-playing and robotics.
A researcher is working with a large dataset of patient medical records with numerous features. They want to visualize the data in 2D to spot any potential patterns or groupings but without necessarily clustering the data. Which technique would they most likely employ?
- Principal Component Analysis
- t-Distributed Stochastic Neighbor Embedding (t-SNE)
- K-Means Clustering
- DBSCAN
The researcher would most likely employ t-Distributed Stochastic Neighbor Embedding (t-SNE). t-SNE is a dimensionality reduction technique suitable for visualizing high-dimensional data in 2D while preserving data relationships and patterns without imposing clusters.
You are given a dataset of customer reviews but without any labels indicating sentiment. You want to group similar reviews together. Which type of learning approach will you employ?
- Reinforcement Learning
- Semi-supervised Learning
- Supervised Learning
- Unsupervised Learning
In this scenario, you will use unsupervised learning. Unsupervised learning is employed when you have unlabelled data and aim to discover patterns or group similar data points without prior guidance.
Why might one choose to use a deeper neural network architecture over a shallower one, given the increased computational requirements?
- Deeper networks can learn more abstract features and improve model performance
- Shallow networks are more computationally efficient
- Deeper networks require fewer training examples
- Deeper networks are less prone to overfitting
Deeper networks can capture complex relationships in the data, potentially leading to better performance. Despite increased computation, they may not always require significantly more training data.
What is the primary purpose of a neural network in machine learning?
- Pattern Recognition
- Sorting and Searching
- Database Management
- Data Visualization
The primary purpose of a neural network is pattern recognition, making it capable of learning complex patterns and relationships in data.
When training a robot to play a game where it gets points for good moves and loses points for bad ones, which learning approach would be most appropriate?
- Reinforcement learning
- Semi-supervised learning
- Supervised learning
- Unsupervised learning
Reinforcement learning is the most appropriate approach for training a robot to play a game where it receives rewards for good moves and penalties for bad ones. In reinforcement learning, the agent learns through trial and error, optimizing its actions to maximize cumulative rewards. Supervised learning would require explicit labels for each move, which are typically not available in this context. Unsupervised and semi-supervised learning are not suitable for tasks with rewards and penalties.
When considering a confusion matrix, which metric calculates the harmonic mean of precision and recall?
- Accuracy
- F1 Score
- Specificity
- True Positive Rate
The F1 Score calculates the harmonic mean of precision and recall. It is useful for situations where there is an uneven class distribution and you want to balance precision and recall.
You're working with a large dataset of facial images. You want to reduce the dimensionality of the images while preserving their primary features for facial recognition. Which neural network structure would you employ?
- Autoencoder
- Convolutional Neural Network
- Recurrent Neural Network
- Generative Adversarial Network
Autoencoders are used to reduce the dimensionality of data while preserving essential features. They are commonly employed in facial recognition for feature extraction.
A spam filter is being designed to classify emails. The model needs to consider the presence of certain words in the email (e.g., "sale," "discount") and their likelihood to indicate spam. Which classifier is more suited for this kind of problem?
- K-Means Clustering
- Naive Bayes
- Random Forest
- Support Vector Machine (SVM)
Naive Bayes is effective for text classification tasks, such as spam filtering, as it models the likelihood of words (e.g., "sale," "discount") indicating spam or non-spam, considering word presence.