Explain how a Decision Tree works in the context of Machine Learning.
- Based on complexity, combines data at each node
- Based on distance, groups data at each node
- Based on entropy, splits data at each node
- Based on gradient, organizes data at each node
A Decision Tree works by splitting the data into subsets based on feature values. This is done recursively at each node by selecting the feature that provides the best split according to a metric like entropy or Gini impurity. The process continues until specific criteria are met, creating a tree-like structure.
How can you tune hyperparameters in SVM to prevent overfitting?
- Changing the color of hyperplane
- Increasing data size
- Reducing feature dimensions
- Using appropriate kernel and regularization
Tuning hyperparameters like the choice of kernel and regularization helps in controlling model complexity to prevent overfitting in SVM.
How does adding regularization help in avoiding overfitting?
- By adding noise to the training data
- By fitting the model closely to the training data
- By increasing model complexity
- By reducing model complexity
Regularization helps in avoiding overfitting by "reducing model complexity." It adds a penalty to the loss function, constraining the weights and preventing the model from fitting too closely to the training data.
The ___________ regression technique can be used when the relationship between the independent and dependent variables is not linear.
- L1 Regularization
- Logistic
- Polynomial
- Simple Linear
Polynomial Regression can model non-linear relationships between independent and dependent variables by transforming the predictors into a polynomial form, allowing for more complex fits.
You reduced the complexity of your model to prevent overfitting, but it led to underfitting. How would you find a balance between complexity and fit?
- Add regularization
- All of the above
- Increase dataset size
- Try cross-validation
Finding a balance might involve using cross-validation to systematically find the right level of complexity that fits well with the training data but also generalizes well to the validation data. This process helps in finding the right hyperparameters without biasing the test data.
How does LDA maximize the separation between different classes in a dataset?
- By maximizing between-class variance and minimizing within-class variance
- By maximizing both within-class and between-class variance
- By minimizing between-class variance and maximizing within-class variance
- By minimizing both within-class and between-class variance
LDA maximizes the separation between different classes by "maximizing between-class variance and minimizing within-class variance." This process ensures that different classes are far apart, while data points within the same class are close together, resulting in better class separation.
_________ clustering builds a tree-like diagram called a dendrogram, allowing you to visualize the relationships between clusters.
- DBSCAN
- Hierarchical
- K-Means
- Spectral
Hierarchical clustering builds a dendrogram, which allows visualization of the relationships between clusters, showing how the clusters are connected.
To detect multicollinearity in a dataset, one common method is to calculate the ___________ Inflation Factor (VIF).
- Validation
- Variable
- Variance
- Vector
The Variance Inflation Factor (VIF) is a measure used to detect multicollinearity. It quantifies how much a variable is inflating the standard errors due to its correlation with other variables. A high VIF indicates multicollinearity.
The risk of overfitting can be increased if the same data is used for both _________ and _________ of the Machine Learning model.
- evaluation, processing
- training, testing
- training, validation
- validation, training
If the same data is used for both "training" and "testing," the model may perform well on that data but poorly on unseen data, leading to overfitting.
You have applied PCA to your dataset and the first three principal components explain 95% of the variance. What does this signify, and how would you proceed?
- This indicates an error in the PCA process
- This means that 5% of the data is missing, so you should reapply PCA
- This means that 95% of the variance is captured, so you may choose to proceed with these components
- This means that the data is uniformly distributed and PCA is not needed
The first three principal components explaining 95% of the variance means that most of the original information is captured, and you may proceed with these components if the loss of 5% is acceptable.
In the context of DBSCAN, if two core points are within the Epsilon distance of each other, they are said to be __________.
- directly dense reachable
- indirectly dense reachable
- separate
- unrelated
In DBSCAN, if two core points are within the Epsilon distance of each other, they are said to be directly dense reachable. This concept is used to establish connections between core points and helps in forming clusters by linking together points that are close to one another.
How can the 'k-distance graph' be used in selecting the optimal Epsilon for DBSCAN?
- By calculating the average distance to k-nearest neighbors
- By determining the distance between k centroids
- By displaying k clusters' distances
- By plotting the distance to the kth nearest neighbor of each point
The 'k-distance graph' can be used to select the optimal Epsilon by plotting the distance to the kth nearest neighbor for each point and looking for an "elbow" or a point of inflection. This inflection point can be a good estimate for Epsilon, helping to choose a value that balances density requirements without overly segmenting the data.