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Data Mining and Decision Support-Lecture,Section-1-Fall 2025
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Among the k-Fold cross validation (CV) and leave-p-out CV techniques, which one belongs to the exhaustive method.
(NOTE: Deducted points for the wrong answer(s). 0 point for 'Not Answering').
Let's assume that a data scientist splits a dataset to training and test datasets using a test size of 0.3; and then he/she adopts a K-fold (k = 5) cross-validation technique. If so, which dataset can be used for a validation set? (NOTE: Deducted points for the wrong answers. 0 point for 'Not Answering').
While high bias and low variance could result in over-fitting, low bias and high variance could result in under-fitting. (NOTE: Deducted points for the wrong answer(s). 0 point for 'Not Answering').
Because bagging techniques use the randomized bootstrap samples, it can’t prevent (or reduce) overfitting problems.
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Let’s assume that we have 3 classifiers and a 3-class classification problems where we assign equal weights to all classifiers: w1 = 1, w2 = 1, w3 = 1; and assume the results of each probability for each classifier are like below. Which class label can be chosen by hard (majority rule) voting.
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To prevent under-fitting, regularization techniques are useful. (NOTE: Deducted points for the wrong answer(s). 0 point for 'Not Answering').
An equation like y = Xh can be solved by inversion of X (i.e., h = X-1y ). And, it can be solved manually or using Python libraries, when the X is square). But, if X is not square or invertible then there is a well known recipe to solve the system approximately called the pseudo inverse (i.e., Ordinary Least Squares, )
Using the below Python references, drag and drop a code for solving the ĥ, assuming X and y are numpy arrays/matrices, and all required modules are imported.
numpy.linalg.inv(a) Compute the (multiplicative) inverse of a matrix.numpy.linalg.pinv(a) //fyi, the second parameter can not be considered Compute the (Moore-Penrose) pseudo-inverse of a matrix.numpy.dot(a, b, out=None) Dot product of two arrays.numpy.transpose(a, axes=None) Permute the dimensions of an array.numpy.ndarray.T Same as self.transpose(), except that self is returned if self.ndim < 2.
You are given data about seismic (caused by an earthquake) activity in Japan, and you want to predict a magnitude of the next earthquake, this is an example of unsupervised learning. (NOTE: Deducted points for the wrong answer(s). 0 point for 'Not Answering').