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Questions and Answers
What does entropy measure in Decision Trees?
In the context of ensemble methods like Random Forest, why are Decision Trees used?
Which algorithm is suitable for visualizing high-dimensional data in lower dimensions?
What is the main function of the 'k' in K-means algorithm?
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Which clustering algorithm can detect arbitrarily shaped clusters?
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How does Decision Trees handle missing values during split evaluation?
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What is the primary use of K-means?
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Which algorithm is sensitive to outliers?
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What does the perplexity parameter control in t-SNE?
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What is the main disadvantage of Decision Trees?
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Which clustering algorithm does not require the user to specify the number of clusters?
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What type of machine learning algorithm is t-SNE?
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Study Notes
Hyperparameters in Algorithms
- The number of nearest neighbors to consider, learning rate, and number of iterations are hyperparameters in algorithms.
Decision Trees in Ensemble Methods
- Decision Trees are used in ensemble methods like Random Forest to reduce variance.
Sensitivity to Input Data Points
- DBSCAN is not sensitive to the order of input data points.
Handling Non-Linear Data
- Decision Trees can handle non-linear data.
K-Means
- The 'k' in K-means represents the number of clusters.
- K-means is primarily used for clustering.
- The number of clusters in K-means is determined by user input.
- K-means is sensitive to outliers.
Visualizing High-Dimensional Data
- t-SNE is suitable for visualizing high-dimensional data in lower dimensions.
- t-SNE can produce a visual representation of the data in 2D or 3D.
Distance Metric
- DBSCAN is sensitive to the choice of distance metric.
Handling Missing Values
- Decision Trees handle missing values by skipping the missing value during split evaluation.
Interpretability
- Decision Trees are known for their interpretability.
Clustering Algorithms
- DBSCAN is a density-based clustering algorithm that can detect arbitrarily shaped clusters.
- DBSCAN does not require the user to specify the number of clusters.
Dimensionality Reduction
- t-SNE is used for reducing the dimensionality of data while preserving the local structure.
Decision Trees
- Entropy is used in Decision Trees to measure impurity.
- Decision Trees can be used for both regression and classification.
- The main disadvantage of Decision Trees is that they are prone to overfitting.
Unsupervised Learning Algorithms
- DBSCAN and t-SNE are unsupervised learning algorithms.
t-SNE
- t-SNE stands for T-distributed Stochastic Neighbor Embedding.
- t-SNE is a type of unsupervised machine learning algorithm.
- The perplexity parameter in t-SNE controls the balance between the attention to local and global aspects of the data.
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Description
Test your knowledge on clustering algorithms such as K-means, DBSCAN, and t-SNE. Learn about the applications and characteristics of these algorithms in machine learning.
