Random Forests introduce randomness in two main ways: by bootstrapping the data and by selecting a random subset of ______ for every split.
- Data Points
- Features
- Leaves
- Trees
Random Forests introduce randomness by selecting a random subset of "Features" for every split in each tree. This helps in creating diverse trees, which collectively improve the overall performance and reduce the risk of overfitting.
The ability of an individual or a group to understand and trust the model's decisions is often tied to the model's ________.
- Explainability
- Complexity
- Accuracy
- Processing speed
Model explainability is essential for understanding and trusting a model's decisions, especially in critical applications like healthcare or finance, where transparency is key for decision-making and accountability.
Which machine learning algorithm is commonly used for time series forecasting due to its ability to remember long sequences?
- Decision Trees.
- Recurrent Neural Networks (RNNs).
- Support Vector Machines (SVMs).
- K-Means Clustering.
Recurrent Neural Networks (RNNs) are favored for time series forecasting because they can remember and model long sequences of data, making them suitable for sequential data like time series.
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.
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.
In the k-NN algorithm, when two classes have a similar number of instances close to a test data point, the choice of an odd 'k' can help to avoid ________
- Ambiguity
- Bias
- Overfitting
- Underfitting
When two classes have a similar number of instances nearby, using an odd 'k' value can help avoid ambiguity in classifying the test data point, as it prevents ties. An even 'k' can lead to ties, making classification less clear.
What term refers to the ability to understand and interpret machine learning model decisions?
- Explainability
- Predictability
- Efficiency
- Generalization
Explainability is the term that refers to the ability to understand and interpret machine learning model decisions. It's crucial for trust and accountability in AI systems.
Which variant of RNN is designed to better capture long-term dependencies in sequence data?
- Vanilla RNN
- Bidirectional RNN
- Long Short-Term Memory (LSTM)
- Gated Recurrent Unit (GRU)
Long Short-Term Memory (LSTM) is designed to capture long-term dependencies in sequences by maintaining cell states that are less susceptible to the vanishing gradient problem.