When dealing with high-dimensional data, which of the two algorithms (k-NN or Naive Bayes) is likely to be more efficient in terms of computational time?
- Both Equally Efficient
- It depends on the dataset size
- Naive Bayes
- k-NN
Naive Bayes is generally more efficient in terms of computational time for high-dimensional data because it doesn't require distance calculations.
Why do traditional RNNs face difficulties in learning long-term dependencies?
- Vanishing Gradient Problem
- Overfitting
- Underfitting
- Activation Function Selection
Traditional RNNs face difficulties due to the "Vanishing Gradient Problem." During backpropagation, gradients can become extremely small, making it challenging to update weights for long sequences. This issue inhibits the model's ability to learn long-term dependencies effectively, a critical limitation in sequence data tasks.
Ridge and Lasso are techniques used for ________ to prevent overfitting.
- Data Preprocessing
- Feature Engineering
- Hyperparameter Tuning
- Regularization
Ridge and Lasso are both regularization techniques used to prevent overfitting in machine learning. Regularization adds penalty terms to the model's loss function to discourage excessive complexity and make the model generalize better.
Which algorithm is commonly used for density estimation in a dataset, especially when modeling clusters as ellipses?
- Gaussian Mixture Model
- k-Means
- Decision Tree
- Support Vector Machine
The Gaussian Mixture Model is frequently used for density estimation. It models data as a mixture of Gaussian distributions, allowing for flexible cluster shapes, including ellipses.
The hidden layer that contains the compressed representation of the input data in an autoencoder is called the ________ layer.
- Bottleneck
- Compression
- Encoding
- Latent
The hidden layer that holds the compressed representation in an autoencoder is the 'Latent' layer, capturing essential features of the input data.
What role do the hidden states in RNNs play in terms of sequential data processing?
- Storing Information Over Time
- Managing Data Loss
- Encoding Input Features
- Updating Weights for Classification
The hidden states in RNNs play a crucial role in storing information over time. They retain memory of past inputs and contribute to the model's ability to process sequential data, making them suitable for tasks with dependencies over time.
Which of the following describes the situation when a model performs well on the training data but poorly on unseen data?
- Bias
- High Variance
- Overfitting
- Underfitting
This situation is known as overfitting, where a model learns to fit the training data too closely but fails to generalize to new, unseen data, resulting in a high error rate.
A model that consistently predicts the same output regardless of the input data is said to have high ________.
- Accuracy
- Consistency
- Precision
- Variability
When a model consistently predicts the same output, it is considered to have high "consistency." This means it's not providing useful or varied predictions, which can be a problem in machine learning.
Hierarchical clustering that starts with each data point as its own cluster and gradually merges them is termed as ________ hierarchical clustering.
- Agglomerative
- Divisive
- Exclusive
- Partitioning
Agglomerative hierarchical clustering is the method where individual data points begin as clusters and then merge step by step based on similarity until they form a single cluster.
Which neural network architecture is specifically designed to work with sequences of data, such as time series or natural language?
- Convolutional Neural Network (CNN)
- Recurrent Neural Network (RNN)
- Long Short-Term Memory (LSTM)
- Gated Recurrent Unit (GRU)
Recurrent Neural Networks (RNNs) are designed for sequential data. They maintain hidden states that capture sequential dependencies, making them suitable for time series and language modeling.