Can you explain the concept of Semi-Supervised Learning and how it bridges the gap between supervised and unsupervised learning?
- Combines labeled & unlabeled data
- Uses only labeled data
- Uses only unlabeled data
- Uses rewards and penalties
Semi-Supervised Learning bridges the gap by combining both labeled and unlabeled data, utilizing strengths of both supervised and unsupervised.
___________ is a popular method for dimensionality reduction that transforms the data into a new coordinate system where the variance is maximized.
- Feature Selection
- Linear Discriminant Analysis
- Principal Component Analysis
- t-SNE
Principal Component Analysis (PCA) is a method that transforms data into a new coordinate system where the variance is maximized. It's a popular technique for reducing dimensions while preserving as much information as possible in the reduced space.
What are some common challenges in high-dimensional data that dimensionality reduction aims to address?
- All of the above
- Computational efficiency
- Curse of dimensionality
- Overfitting
Dimensionality reduction aims to address several challenges in high-dimensional data, including the curse of dimensionality (where distance measures lose meaning), overfitting (where models fit noise), and computational efficiency (since fewer dimensions require less computing resources).
How does Principal Component Analysis (PCA) work as a method of dimensionality reduction?
- By classifying features
- By maximizing variance
- By minimizing variance
- By selecting principal features
Principal Component Analysis (PCA) works by transforming the original features into a new set of uncorrelated features called principal components. It does so by maximizing the variance along these new axes, meaning that the first principal component explains the most variance, the second explains the second most, and so on.
Poor initialization of centroids in K-Means clustering may lead to __________, affecting the quality of the clustering.
- Convergence to global maxima
- Local minima
- Noise
- Overfitting
Poor initialization of centroids can lead the K-Means algorithm to converge to local minima, affecting the quality of the clustering. Local minima occur when the algorithm finds a suboptimal clustering solution.
If you are facing multicollinearity in your regression model, how can dimensionality reduction techniques be employed to mitigate this issue?
- Increase the number of observations
- Apply PCA and use principal components
- Add interaction terms
- Use a non-linear regression model
Multicollinearity arises when features are highly correlated with each other, and it can be mitigated by applying PCA. By transforming the data into principal components, which are uncorrelated, the multicollinearity problem is resolved. Using the principal components in the regression model ensures that the feature relationships are captured without redundancy. Other options do not address the issue of multicollinearity directly.
In Polynomial Regression, a higher degree can lead to ________, where the model learns the noise in the data.
- accuracy
- overfitting
- stability
- underfitting
A higher degree in Polynomial Regression may cause the model to fit the noise in the data, leading to overfitting.
Interaction effects in Multiple Linear Regression can be represented by adding a ___________ term for the interacting variables.
- additive
- divided
- multiplied
- subtractive
Interaction effects are represented by adding a multiplied term for the interacting variables in the model. It captures the combined effect that is not simply additive and reflects how the response variable changes when both interacting variables change together.
The slope of your Simple Linear Regression model is close to zero, but the intercept is significant. What does this indicate, and what could be the potential reason?
- Error in Model, Incorrect Data
- No Relationship, Constant Value of Dependent Variable
- Strong Relationship, Outliers
- Weak Relationship, Lack of Variation in Independent Variable
A slope close to zero may indicate a weak or no relationship between the variables, and this could be due to a lack of variation in the independent variable.
How does ElasticNet combine the properties of both Ridge and Lasso regularization?
- Does not combine properties
- Uses L1 penalty only
- Uses L2 penalty only
- Uses both L1 and L2 penalties
Elastic Net combines both L1 and L2 penalties, thus including properties of both Ridge (L2) and Lasso (L1) regularization.
How does K-Means clustering respond to non-spherical data distributions, and how can initialization affect this?
- Adapts well to non-spherical data
- Performs equally well with all data shapes
- Struggles with non-spherical data; Initialization can alleviate this
- Struggles with non-spherical data; Initialization has no effect
K-Means tends to struggle with non-spherical data distributions since it relies on Euclidean distance. Careful initialization can partially alleviate this issue but cannot fully overcome the fundamental limitation.
__________ learning utilizes both labeled and unlabeled data, often leveraging the strengths of both supervised and unsupervised learning.
- reinforcement
- semi-supervised
- supervised
- unsupervised
Semi-Supervised learning combines both labeled and unlabeled data, leveraging the strengths of both supervised and unsupervised learning.