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Machine Learning
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Q201

A 4-input neuron has weights 1, 2, 3 and 4. The transfer function is linear with the constant of proportionality being equal to 2. The inputs are 4, 10, 10 and 30 respectively. What will be the output?

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Q202

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

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Q203

Which of the following metrics can be used for evaluating regression models?
1. R Squared
2. Adjusted R Squared
3. F Statistics
4. RMSE / MSE / MAE

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Q204

What do you mean by generalization error in terms of the SVM?

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Q205

In order to assess how much information is brought by each component, and the correlation among them, a useful tool is the . . . . . . . .

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Q206

For a multiple regression model, SST = 200 and SSE = 50. The multiple coefficient of determination is

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Q207

Which of the following method(s) does not have closed form solution for its coefficients?

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Q208

Which of the following model model include a backwards elimination feature selection routine?

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Q209

How is the model capacity affected with dropout rate (where model capacity means the ability of a neural network to approximate complex functions)?

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Q210

During the treatement of cancer patients , the doctor needs to be very careful about which patients need to be given chemotherapy.Which metric should we use in order to decide the patients who should given chemotherapy?

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Q211

The SVM's are less effective when:

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Q212

The process of forming general concept definitions from examples of concepts to be learned.

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Q213

What is 'Test set'?

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Q214

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

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Q215

Selecting data so as to assure that each class is properly represented in both the training and test set.

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Q216

Which of the following scale data by removing elements that don't belong to a given range or by considering a maximum absolute value.

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Q217

Which of the following selects the best K high-score features.

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Q218

Which of the following statements are true for a design matrix X ∈ Rn×d with d > n? (The rows are n sample points and the columns represent d features.)

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Q219

Attribute selection measures are also known as splitting rules.

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Q220

. . . . . . . . produce sparse matrices of real numbers that can be fed into any machine learning model.

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