Q341
Open questionWhat are tree based classifiers?
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Computer Science · all questionsMachine Learning
Machine Learning
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Q342
Open questionSuppose you are given 'n' predictions on test data by 'n' different models (M1, M2, .... Mn) respectively. Which of the following method(s) can be used to combine the predictions of these models?
Note: We are working on a regression problem
1. Median
2. Product
3. Average
4. Weighted sum
5. Minimum and Maximum
6. Generalized mean rule
Note: We are working on a regression problem
1. Median
2. Product
3. Average
4. Weighted sum
5. Minimum and Maximum
6. Generalized mean rule
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Q343
Open questionThe "curse of dimensionality" referes
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Q344
Open questionWhat is Decision Tree?
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Q345
Open questionWhich one of these is a tree based learner?
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Q346
Open questionIt's possible to specify if the scaling process must include both mean and standard deviation using the parameters . . . . . . . .
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Q347
Open questionConsider the following dataset. x,y,z are the features and T is a class(1/0). Classify the test data (0,0,1) as values of x,y,z respectively.
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Q348
Open questionThe term . . . . . . . . can be freely used, but with the same meaning adopted in physics or system theory.
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Q349
Open questionBootstrapping allows us to
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Q350
Open questionWhat can be major issue in Leave-One-Out-Cross-Validation(LOOCV)?
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Q351
Open questionWhat is the actual number of independent parameters which need to be estimated in P dimensional Gaussian distribution model?
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Q352
Open questionSolving a non linear separation problem with a hard margin Kernelized SVM (Gaussian RBF Kernel) might lead to overfitting
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Q353
Open questionWhich Association Rule would you prefer
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Q354
Open questionSupervised learning differs from unsupervised clustering in that supervised learning requires
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Q355
Open questionType of matrix decomposition model is
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Q356
Open questionBayes Theorem is given by where
1. P(H) is the probability of hypothesis H being true.
2. P(E) is the probability of the evidence(regardless of the hypothesis).
3. P(E|H) is the probability of the evidence given that hypothesis is true.
4. P(H|E) is the probability of the hypothesis given that the evidence is there.
1. P(H) is the probability of hypothesis H being true.
2. P(E) is the probability of the evidence(regardless of the hypothesis).
3. P(E|H) is the probability of the evidence given that hypothesis is true.
4. P(H|E) is the probability of the hypothesis given that the evidence is there.
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Q357
Open questionWe have been given a dataset with n records in which we have input attribute as x and output attribute as y. Suppose we use a linear regression method to model this data. To test our linear regressor, we split the data in training set and test set randomly. What do you expect will happen with bias and variance as you increase the size of training data?
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Q358
Open questionWhich of the following is true about Residuals ?
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Q359
Open questionPrediction is
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Q360
Open questionFunction used for linear regression in R is
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