What would you do in PCA to get the same projection as SVD?
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The . . . . . . . . of the hyperplane depends upon the number of features.
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What is the approach of basic algorithm for decision tree induction?
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Can we extract knowledge without apply feature selection
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Suppose there are 25 base classifiers. Each classifier has error rates of e = 0.35. Suppose you are using averaging as ensemble technique. What will be the probabilities that ensemble of above 25 classifiers will make a wrong prediction? Note: All classifiers are independent of each other
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When the number of classes is large Gini index is not a good choice.
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Data used to build a data mining model.
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This technique associates a conditional probability value with each data instance.
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What is the purpose of the Kernel Trick?
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Having multiple perceptrons can actually solve the XOR problem satisfactorily: this is because each perceptron can partition off a linear part of the space itself, and they can then combine their results.
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Can we calculate the skewness of variables based on mean and median?
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It is possible to design a Linear regression algorithm using a neural network?
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If Linear regression model perfectly first i.e., train error is zero, then . . . . . . . .
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Increase in size of a convolutional kernel would necessarily increase the performance of a convolutional network.
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To control the size of the tree, we need to control the number of regions. One approach to do this would be to split tree nodes only if the resultant decrease in the sum of squares error exceeds some threshold. For the described method, which among the following are true?
a. It would, in general, help restrict the size of the trees
b. It has the potential to affect the performance of the resultant regression/classification model
c. It is computationally infeasible
a. It would, in general, help restrict the size of the trees
b. It has the potential to affect the performance of the resultant regression/classification model
c. It is computationally infeasible
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Which of the following is the difference between stacking and blending?
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Logistic regression is a . . . . . . . . regression technique that is used to model data having a . . . . . . . . outcome.
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You are given reviews of few netflix series marked as positive, negative and neutral. Classifying reviews of a new netflix series is an example of
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In following type of feature selection method we start with empty feature set
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Neural Networks are complex . . . . . . . . with many parameters.
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