The DBSCAN algorithm groups together points that are closely packed, forming clusters, and treats more isolated points as _________.
- Centroids
- Clusters
- Noise
- Outliers
In DBSCAN, more isolated points that don't belong to any cluster are treated as outliers.
How is the amount of variance explained related to Eigenvalues in PCA?
- Eigenvalues are unrelated to variance
- Eigenvalues represent the mean of the data
- Larger eigenvalues explain more variance
- Smaller eigenvalues explain more variance
In PCA, the amount of variance explained by each principal component is directly related to its corresponding eigenvalue. Larger eigenvalues mean that more variance is explained by that particular component.
You're working with a dataset where different features are on wildly different scales. How can dimensionality reduction techniques like PCA be adapted to this scenario?
- Apply PCA without any preprocessing
- Ignore the scales
- Scale the features before applying PCA
- Use only large-scale features
When features are on different scales, scaling them before applying PCA is crucial. Standardizing the features ensures that each one contributes equally to the calculation of the principal components, which is vital for the accuracy of the transformation. Ignoring the scales, applying PCA without preprocessing, or focusing only on large-scale features may lead to biased or incorrect results.
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.
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.
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.
_________ 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.
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.