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.
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.
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.
An online platform wants to generate new, realistic profile pictures for users who don't want to upload their own photos. They aim for these generated images to be indistinguishable from real photos. Which technology can achieve this?
- Generative Adversarial Networks (GAN)
- Principal Component Analysis (PCA)
- Logistic Regression
- K-Means Clustering
Generative Adversarial Networks (GAN) are designed to generate synthetic data that is highly realistic. In the context of generating profile pictures, GANs can produce images that are often indistinguishable from real photos, making them an ideal choice for this task.
The main advantage of Deep Q Networks over traditional Q-learning is their ability to handle high-dimensional ________ spaces.
- State
- Action
- Observation
- Feature
Deep Q Networks are advantageous due to their capability to handle high-dimensional observation spaces. This is crucial when dealing with complex real-world data, as in image-based environments.