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Below are the two ensemble models: 1. E1(M1, M2, M3) and 2. E2(M4, M5, M6) Above, Mx is the individual base models. Which of the following are more likely to choose if following conditions for E1 and E2 are given? E1: Individual Models accuracies are high but models are of the same type or in another term less diverse E2: Individual Models accuracies are high but they are of different types in another term high diverse in nature
Regarding bias and variance, which of the following statements are true? (Here 'high' and 'low' are relative to the ideal model.
i. Models which overfit are more likely to have high bias
ii. Models which overfit are more likely to have low bias
iii. Models which overfit are more likely to have high variance
iv. Models which overfit are more likely to have low variance
Regarding bias and variance, which of the following statements are true? (Here 'high' and 'low' are relative to the ideal model. (i) Models which overfit are more likely to have high bias (ii) Models which overfit are more likely to have low bias (iii) Models which overfit are more likely to have high variance (iv) Models which overfit are more likely to have low variance
In the following question, the 1st and the last part of the sentence/passage are numbered 1 and 6. The rest of the sentence/ passage is split into four parts and named P, Q, R and S. These four parts are not given in their proper order. Read the sentence/passage and find out which of the four combinations is correct. 1. If you are a poet, you will have no difficulty seeing the cloud floating in this sheet of paper. P. So we can say that the cloud and the paper are inter-related. Q. That explains why the cloud is essential for the paper to exist. R.  If the cloud is not here, the sheet of paper cannot be here either.  S. If there are no clouds, there will be no rain; and if there is no rain, the trees will not grow; and if there are no trees, we cannot make paper. 6.  If we look into this sheet of paper even more deeply, we can see the sunshine in it. 
There are two rows, each containing 3 numbers. Below are some rules to be used to get the resultant number for each row separately. Use the rule from left to right for each row and find the answer to the question. Rule: 1. If an even number is followed by an even number, then the first number will be subtracted from the second. 2. If an odd number is followed by an odd number, then both will be added. 3. If an odd number is followed by an even number, then the even number will be subtracted from the square of the odd number. 4. If an even number is followed by an odd number, then the odd number will be subtracted from the even number. Row I: 9,11, 34 Row II: 16, 9, 8 What is the difference between the resultant numbers in the first row and the second row
In a car showroom different models of car are on display. There are two models of 800 cc car and four models of 1500 cc car. What is the probability that a customer will choose am 800 cc car?
Suppose, you want to apply a stepwise forward selection method for choosing the best models for an ensemble model. Which of the following is the correct order of the steps? Note: You have more than 1000 models predictions.
1. Add the models predictions (or in another term take the average) one by one in the ensemble which improves the metrics in the validation set.
2. Start with empty ensemble
3. Return the ensemble from the nested set of ensembles that has maximum performance on the validation set
Suppose, you have 2000 different models with their predictions and want to ensemble predictions of best x models. Now, which of the following can be a possible method to select the best x models for an ensemble?
Which of the following is true about weighted majority votes?
1. We want to give higher weights to better performing models
2. Inferior models can overrule the best model if collective weighted votes for inferior models is higher than best model
3. Voting is special case of weighted voting
Suppose you are using stacking with n different machine learning algorithms with k folds on data. Which of the following is true about one level (m base models + 1 stacker) stacking? Note: Here, we are working on binary classification problem All base models are trained on all features You are using k folds for base models