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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
Which of the following can be true for selecting base learners for an ensemble?
1. Different learners can come from same algorithm with different hyper parameters
2. Different learners can come from different algorithms
3. Different learners can come from different training spaces
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
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?
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
In an election, N candidates are competing against each other and people are voting for either of the candidates. Voters don't communicate with each other while casting their votes. Which of the following ensemble method works similar to above-discussed election procedure? Hint: Persons are like base models of ensemble method.
You are the administrator of a Windows 2000 Server computer that has one hard disk. This computer runs a custom application that writes a large number of small temporary files in a single directory to support request from client computers. To improve performance of the application, you add three new 100-GB SCSI disks to the server to hold these temporary files. You want to ensure that the application can use all 300 GB of space with a single drive letter. You also want to ensure the fastest possible performance when writing the temporary files. How should you configure the three disks?
Generally, an ensemble method works better, if the individual base models have . . . . . . . .?
Note: Suppose each individual base models have accuracy greater than 50%.
In the following passage, some of the words have been left out. Read the passage carefully and select the correct answer for the given blank out of the fouralternatives.
Job performance is ______ by a number of factors. Motivation alone does not lead to increased performance. Ability and technology moderates the relationship
between motivation and performance. The higher the levels of ability and motivation the ______ the level of performance will be. However, increasing motivation
beyond an ______ level tends to ______ a dysfunctional result because it is ______ by an increased level of anxiety.
motivation and performance. The higher the levels of ability and motivation the ______ the level of performance will be.
How can we assign the weights to output of different models in an ensemble?
1. Use an algorithm to return the optimal weights
2. Choose the weights using cross validation
3. Give high weights to more accurate models