Lasso can be interpreted as least-squares linear regression where 

• weights are regularized with the L1 norm

• the weights have a Gaussian prior

• weights are regularized with the L2 norm

• the solution algorithm is simpler e. None of the above

What is overfitting? 

• Great result in training and poor result in test

• Great result in training and great result in test

• Poor result in training and poor result in test

 ... shows how far is a ?average model? from the ground truth 

• Bias

• MSE

• Variance

• R squared

Which of the following techniques can be used for keyword normalization in NLP, the process of converting a keyword into its base form?

• Lemmatization

• Soundex

• Cosine Similarity

• N-grams

Which of the following indicates a fairly strong relationship between X and Y?

• Correlation coefficient = 0.9

• The p-value for the null hypothesis Beta coefficient =0 is 0.0001

• The t-statistic for the null hypothesis Beta coefficient =0 is 30

• None of the above

You trained a binary classifier model which gives very high accuracy on the training data, but much lower accuracy on validation data. The following may be true:

• This is an instance of overfitting

• This is an instance of underfitting.

• The training was not well regularized.

• The training and testing examples are sampled from different distributions

Which way Lasso Regression differs from Ridge Regression? 

• It uses absolute values in regularization parameter, instead of squares

• It uses square values in regularization parameter

• It works better in small datasets

• It works better in big datasets

  None of the above

What kind of function is used to predict in the sklearn library? 

• predict()

• test()

• find()

• None of the above

Which is not a sample of classification problem?

• To predict the amount of money a customer will spend in one year

• To predict whether a customer switches to another provider or brand

• To predict the category to which customer belongs to

• To predict whether a customer responds to a particular advertising campaign or not

Regarding bias and variance, which of the following statements are true? (select multiple)

• Models which overfit have a high bias.

• Models which overfit have a low bias.

• Models which underfit have a low variance.

• None of the above