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.
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.
How does Random Forest handle missing values during the training process?
- Both imputation using mean/median and using random values
- Ignores missing values completely
- Randomly selects a value
- Uses the mean or median for imputation
Random Forest can handle missing values by using mean or median imputation for numerical attributes and random value selection or mode imputation for categorical ones. This flexibility helps in maintaining robustness without losing significant data.
Imagine you have a Decision Tree that is overfitting the training data. How would you apply pruning to address this issue?
- Ignore irrelevant features
- Increase tree depth
- Remove irrelevant branches
- Use the entire dataset for training
Pruning involves removing branches that have little predictive power, reducing the model's complexity and sensitivity to noise in the training data. By removing irrelevant branches, the overfitting issue can be mitigated, and the model may generalize better to unseen data.
You have applied PCA to a dataset and obtained principal components. How would you interpret these components, and what do they represent?
- They represent individual original features
- They represent clusters within the data
- They represent the variance in specific directions
- They represent correlations between features
Principal components represent the directions in the data where the variance is maximized. They are linear combinations of the original features and capture the essential patterns, making it possible to describe the dataset in fewer dimensions without significant loss of information. The other options are incorrect as principal components do not directly represent individual original features, clusters, or correlations.
How are convolutional neural networks (CNNs) used in image recognition applications?
- Analyzing Financial Data
- Drug Development
- Managing Energy Systems
- Recognizing Patterns in Images
Convolutional Neural Networks (CNNs) are designed to recognize patterns within images. They use convolutional layers to automatically learn spatial hierarchies of features, making them highly effective in image recognition tasks.
__________ 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.
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.
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.
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.