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.
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.
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.
What is the primary challenge addressed by the multi-armed bandit problem?
- Balancing Exploration and Exploitation
- Image Recognition
- Language Translation
- Voice Assistant Development
The primary challenge of the multi-armed bandit problem is to balance Exploration (trying new actions) and Exploitation (choosing known good actions) to maximize cumulative rewards in a limited time.
In logistic regression, the log odds of the dependent variable is modeled as a linear combination of the independent variables using the ________ function.
- Hypothesis
- Logit
- Probability
- Sigmoid
In logistic regression, the log odds of the dependent variable is modeled using the Logit function. The Logit function is the inverse of the sigmoid function and is used to map linear combinations of independent variables to the range of real numbers.
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.