Podcast
Questions and Answers
Which algorithm is sensitive to the initialization of cluster centroids?
Which algorithm is sensitive to the initialization of cluster centroids?
Which algorithm is used for feature selection?
Which algorithm is used for feature selection?
Which algorithm is used for imputing missing values?
Which algorithm is used for imputing missing values?
Which algorithm is prone to the curse of dimensionality?
Which algorithm is prone to the curse of dimensionality?
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Which algorithm uses a dendrogram to represent the hierarchy of clusters?
Which algorithm uses a dendrogram to represent the hierarchy of clusters?
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Which algorithm is computationally expensive for large datasets?
Which algorithm is computationally expensive for large datasets?
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Which algorithm is used for reducing the number of features?
Which algorithm is used for reducing the number of features?
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Which algorithm has a parameter called 'min_samples'?
Which algorithm has a parameter called 'min_samples'?
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Which algorithm can handle non-convex clusters?
Which algorithm can handle non-convex clusters?
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Which algorithm is based on the concept of proximity?
Which algorithm is based on the concept of proximity?
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What is the main drawback of K-means clustering?
What is the main drawback of K-means clustering?
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Which algorithm is sensitive to the choice of initial cluster centroids?
Which algorithm is sensitive to the choice of initial cluster centroids?
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What is the primary use of DBSCAN?
What is the primary use of DBSCAN?
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Which algorithm is not a clustering algorithm?
Which algorithm is not a clustering algorithm?
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What problem is t-SNE designed to solve?
What problem is t-SNE designed to solve?
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In which type of learning is the number of clusters not pre-specified?
In which type of learning is the number of clusters not pre-specified?
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Which algorithm is sensitive to outliers?
Which algorithm is sensitive to outliers?
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What does the perplexity parameter control in t-SNE?
What does the perplexity parameter control in t-SNE?
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In Random Forest, what is the main purpose of using Decision Trees?
In Random Forest, what is the main purpose of using Decision Trees?
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Which clustering algorithm is not sensitive to the order of input data points?
Which clustering algorithm is not sensitive to the order of input data points?
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Among the algorithms listed, which one can handle non-linear data effectively?
Among the algorithms listed, which one can handle non-linear data effectively?
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What does the 'k' in K-means clustering represent?
What does the 'k' in K-means clustering represent?
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Which algorithm is suitable for visualizing high-dimensional data in lower dimensions?
Which algorithm is suitable for visualizing high-dimensional data in lower dimensions?
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In Decision Trees, what is the primary purpose of using entropy?
In Decision Trees, what is the primary purpose of using entropy?
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