Which of the following parameters can be tuned for finding good ensemble model in bagging based algorithms? 1. Max number of samples 2. Max features 3. Bootstrapping of samples 4. Bootstrapping of features

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Which of the following parameters can be tuned for finding good ensemble model in bagging based algorithms?
1. Max number of samples
2. Max features
3. Bootstrapping of samples
4. Bootstrapping of features

1.1

2.2

3. 3&4

4.1,2,3&4

Posted Date:-2022-06-22 09:29:25

More MCQS Questions and answers

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  2. In Naive Bayes equation P(C / X)= (P(X / C) *P(C) ) / P(X) which part considers "likelihood"?
  3. In which neural net architecture, does weight sharing occur?
  4. Skewness of Normal distribution is ___________
  5. What is the sequence of the following tasks in a perceptron? Initialize weights of perceptron randomly Go to the next batch of dataset If the prediction does not match the output, change the weights For a sample input, compute an output
  6. Which of the following is an application of NN (Neural Network)?
  7. Which of the following is true about Manhattan distance?
  8. Which one of the following is not a major strength of the neural network approach?
  9. Which Statement is not true statement.
  10. 8 observations are clustered into 3 clusters using K-Means clustering algorithm. After first iteration clusters, C1, C2, C3 has following observations: C1: {(2,2), (4,4), (6,6)} C2: {(0,4), (4,0),(2,5)} C3: {(5,5), (9,9)} What will be the cluster centroids if you want to proceed for second iteration?
  11. A company has build a kNN classifier that gets 100% accuracy on training data. When they deployed this model on client side it has been found that the model is not at all accurate. Which of the following thing might gone wrong?Note: Model has successfully deployed and no technical issues are found at client side except the model performance
  12. Consider 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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  17. Hierarchical clustering is slower than non-hierarchical clustering?
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  21. How the entries in the full joint probability distribution can be calculated?
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  23. In which of the following cases will K-Means clustering fail to give good results?
  24. In which of the following cases will K-means clustering fail to give good results? 1) Data points with outliers 2) Data points with different densities 3) Data points with nonconvex shapes
  25. Neural Networks are complex ______________ with many parameters.
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  29. The F-test
  30. The K-means algorithm:
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  39. What is gini index?
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