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SVMs (Support vector machines) are a binary classification method to discriminate one set of data points from another.

SVMs (Support vector machines) are a binary classification method to discriminate one set of data points from another. Correct Answer True

They are similar to the types of discriminant analyses. For microarray analysis, sets of genes are identified that represent a target pattern of gene expression.

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Suppose on performing reduced error pruning, we collapsed a node and observed an improvement in the prediction accuracy on the validation set. Which among the following statements are possible in light of the performance improvement observed? (a) The collapsed node helped overcome the effect of one or more noise affected data points in the training set (b) The validation set had one or more noise affected data points in the region corresponding to the collapsed node (c) The validation set did not have any data points along at least one of the collapsed branches (d) The validation set did have data points adversely affected by the collapsed node
In SVMs (Support vector machines) Data points are log-transformed and normalized as in method A, where for N observations of a gene i, the log transform Xi of the expression level Ei and reference level Ri is?
Which of the following statements are true?
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Statement II Big data interests industries because they are able to learn more patterns and curious variances than smaller data sets, with the capability to provide valuable insights into consumer behaviour, weather systems, financial market activity, or other phenomena.
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In the context of machine learning, what is the purpose of the "kernel trick" in support vector machines (SVMs)?
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which of the following cases will K-Means clustering give poor results?
1. Data points with outliers
2. Data points with different densities
3. Data points with round shapes
4. Data points with non-convex shapes
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1. Data points with outliers
2. Data points with different densities
3. Data points with round shapes
4. Data points with non-convex shapes
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