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.
When it comes to classifying data points, the _________ algorithm considers the 'K' closest points to make a decision.
- K-Nearest Neighbors (KNN)
- Logistic Regression
- Random Forest
- Support Vector Machines
K-Nearest Neighbors (KNN) algorithm classifies a data point based on the majority class of its 'K' closest points in the dataset, using distance metrics to determine proximity.
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.
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.
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.
Bootstrapping involves resampling with replacement from the dataset to create "n" _________ datasets.
- additional
- bootstrap
- copied
- resampled
Bootstrapping is a statistical method that involves resampling with replacement from the dataset to create "n" "bootstrap" datasets. It allows estimating the distribution of a statistic by creating many resampled datasets and calculating the statistic for each.
How can you evaluate the performance of an LDA model?
- By checking the size of the scatter matrices
- By comparing with PCA
- Using confusion matrix and ROC curves
- Using only accuracy
The performance of an LDA model can be evaluated using metrics like the "confusion matrix and ROC curves." These tools provide insights into the model's ability to classify instances correctly and its trade-off between sensitivity and specificity.
You applied DBSCAN and found that many points are being classified as noise. What adjustments could you make to the parameters?
- Decrease Epsilon; Increase MinPts
- Increase Epsilon; Decrease MinPts
- Increase both Epsilon and MinPts
- Use the same Epsilon and MinPts but change the clustering method
Increasing Epsilon and decreasing MinPts will make the clustering less strict, reducing the chance of points being classified as noise. Epsilon defines the neighborhood size, and MinPts defines the minimum points required to form a cluster. By adjusting them, more points can be included in clusters, reducing noise classification.
In K-Means clustering, a common approach to avoid local minima due to initial centroid selection is to run the algorithm multiple times with different _________.
- Centroid initializations
- Distance metrics
- Learning rates
- Number of clusters
Running the K-Means algorithm multiple times with different centroid initializations helps in avoiding local minima. It increases the chance of finding a more globally optimal clustering solution.
What is the role of cross-validation in detecting and preventing overfitting in Polynomial Regression?
- It assists in increasing model complexity
- It focuses on training data only
- It helps in choosing the right degree and assessing generalization
- It helps in selecting features
Cross-validation plays a key role in detecting and preventing overfitting in Polynomial Regression by helping in choosing the right degree for the polynomial and assessing how well the model generalizes to new data.