How do AI, Machine Learning, and Deep Learning differ in terms of data volume and data quality requirements?
- AI requires least data, followed by Machine Learning and Deep Learning
- AI requires most data, followed by Machine Learning and Deep Learning
- Deep Learning requires most data, Machine Learning less, and AI least
- Machine Learning requires most data, followed by AI and Deep Learning
Deep Learning usually requires the most data to train effectively, followed by Machine Learning, with AI generally requiring the least (since AI includes non-learning methods).
In what situations would ElasticNet be preferred over Ridge or Lasso?
- When all features are equally important
- When features are uncorrelated
- When model complexity is not a concern
- When multicollinearity is high
ElasticNet is preferred when there's multicollinearity and you want to balance between Ridge and Lasso, as it combines the properties of both.
The ________ component in PCA explains the highest amount of variance within the data.
- first
- last
- median
- random
The "first" principal component in PCA explains the highest amount of variance within the data. It is aligned with the direction of the maximum spread of the data and forms the most substantial part of the dataset's structure.
How is the amount of variance explained related to Eigenvalues in PCA?
- Eigenvalues are unrelated to variance
- Eigenvalues represent the mean of the data
- Larger eigenvalues explain more variance
- Smaller eigenvalues explain more variance
In PCA, the amount of variance explained by each principal component is directly related to its corresponding eigenvalue. Larger eigenvalues mean that more variance is explained by that particular component.
You're working with a dataset where different features are on wildly different scales. How can dimensionality reduction techniques like PCA be adapted to this scenario?
- Apply PCA without any preprocessing
- Ignore the scales
- Scale the features before applying PCA
- Use only large-scale features
When features are on different scales, scaling them before applying PCA is crucial. Standardizing the features ensures that each one contributes equally to the calculation of the principal components, which is vital for the accuracy of the transformation. Ignoring the scales, applying PCA without preprocessing, or focusing only on large-scale features may lead to biased or incorrect results.
What is Machine Learning and why is it important?
- A brand of computer
- A field of AI that learns from experience
- A study of computers
- A type of computer virus
Machine Learning is a subset of artificial intelligence that focuses on the development of algorithms and statistical models that enable computers to perform specific tasks without explicit instructions. It's important because it allows systems to learn from data, adapt, and improve over time, making it essential in fields like healthcare, finance, transportation, and more.
You are working on a real-world problem that requires clustering, but the Elbow Method doesn't show a clear elbow point. What might be the underlying issues, and how could you proceed?
- Data doesn't have well-separated clusters; Consider other methods like Silhouette
- Increase the number of data points
- Reduce the number of features
- Use a different clustering algorithm entirely
When the Elbow Method doesn't show a clear elbow point, it may be an indication that the data doesn't have well-separated clusters. In this case, considering other methods like the Silhouette Method to determine the optimal number of clusters is an appropriate course of action.
Explain the concept of regularization in Machine Learning. What are some common techniques?
- Increasing complexity, Gradient Boosting
- Increasing complexity, L1/L2
- Reducing complexity, Gradient Descent
- Reducing complexity, L1/L2
Regularization is a technique to reduce overfitting by adding a penalty term to the loss function. Common techniques include L1 (lasso) and L2 (ridge) regularization, which penalize large coefficients in a model.
In a dataset with fluctuating values, you've applied Polynomial Regression, and the model seems to fit even the noise. What are the potential risks, and how could you mitigate them?
- Add more noise
- Ignore the noise
- Reduce model complexity through lower degree or regularization
- Use a linear model
The risk is overfitting the noise, which will harm the model's generalization ability. Reducing the polynomial degree or using regularization techniques can mitigate this by constraining the model's complexity.
How does Deep Learning model complexity typically compare to traditional Machine Learning models, and what are the implications of this?
- Less complex and easier to train
- Less complex and requires less data
- More complex and easier to interpret
- More complex and requires more data and computation
Deep Learning models are typically more complex, requiring more data and computational resources, which can make training and tuning more challenging.