A company wants to classify emails as either spam or not spam. What would be your approach to create a classification model for this problem?
- Ignore the email content; focus on sender details
- Use only email metadata
- Use text mining techniques to extract features; use suitable classification algorithm
- Use unsupervised learning
Extracting relevant features from the email content using text mining techniques and applying a suitable classification algorithm (e.g., Naive Bayes, SVM) would be an effective approach for spam email classification.
The _________ is a crucial aspect of a Machine Learning model that quantifies how well the model's predictions match the actual targets.
- Activation function
- Learning rate
- Loss function
- Optimization algorithm
The loss function quantifies the difference between the predicted values and the actual targets, guiding the learning process.
In the context of regression, the relationship between the independent variable and the dependent variable is represented by a mathematical equation called a _________.
- Linear Equation
- Model
- Polynomial Equation
- Regression Equation
The relationship between the independent variable and the dependent variable in regression is represented by a regression equation, which describes how the dependent variable changes as the independent variable changes.
After applying PCA to your dataset, you find that some Eigenvectors have very small corresponding Eigenvalues. What does this indicate, and what action might you take?
- This indicates a problem with the data and you must discard it
- This indicates that these eigenvectors capture little variance, and you may choose to discard them
- This is an indication that PCA is not suitable for your data
- This means that you must include these eigenvectors
Very small eigenvalues indicate that the corresponding eigenvectors capture little variance, and discarding them would reduce dimensions without losing much meaningful information.
What are some common techniques to avoid overfitting?
- Increasing model complexity, Adding noise, Cross-validation
- Increasing model complexity, Regularization, Cross-validation
- Reducing model complexity, Adding noise, Cross-validation
- Reducing model complexity, Regularization, Cross-validation
Common techniques to avoid overfitting include "reducing model complexity, regularization, and cross-validation." These methods prevent the model from fitting too closely to the training data.
You're designing a self-driving car's navigation system. How would reinforcement learning be applied in this context?
- To cluster traffic patterns
- To combine labeled and unlabeled data
- To learn optimal paths through rewards/penalties
- To use only labeled data for navigation
Reinforcement Learning would enable the navigation system to learn optimal paths by interacting with the environment and receiving feedback through rewards and penalties.
In a high-dimensional dataset, how would you decide which kernel to use for SVM?
- Always use RBF kernel
- Always use linear kernel
- Choose the kernel randomly
- Use cross-validation to select the best kernel
By using cross-validation, you can compare different kernels' performance and choose the one that gives the best validation accuracy.
In what scenarios would you use PCA, and when would you opt for other methods like LDA or t-SNE?
- Use PCA for high-dimensional data, LDA for linearly separable, t-SNE for non-linear
- Use PCA for labeled data, LDA for unlabeled, t-SNE for large-scale
- Use PCA for large-scale, LDA for visualization, t-SNE for labeled data
- Use PCA for noisy data, LDA for small-scale, t-SNE for visualizations
Use PCA when dealing with high-dimensional data and the primary goal is to reduce dimensions by maximizing variance. LDA is suitable when class labels are available, and the data is linearly separable. t-SNE is often used for non-linear data and is especially useful for visualizations, as it preserves local structures.
You are asked to include an interaction effect between two variables in a Multiple Linear Regression model. How would you approach this task, and what considerations would you need to keep in mind?
- Add the variables
- Divide the variables
- Multiply the variables and include the interaction term in the model
- Multiply the variables together
Including an interaction effect involves multiplying the variables together and adding this interaction term to the model. It's important to consider the meaningfulness of the interaction, possible multicollinearity with other variables, and the potential need for centering the variables to minimize issues with interpretation.
In a scenario where your model is consistently achieving mediocre performance on both training and validation data, what might be the underlying problem, and what would be your approach to fix it?
- Increase complexity
- Overfitting, reduce complexity
- Reduce complexity
- Underfitting, add complexity
The underlying problem might be underfitting, where the model is too simple to capture the underlying patterns. Increasing the model's complexity would likely improve performance on both training and validation data.