ML Quiz 10

__________ is a machine learning technique that iteratively combines a set of simple and not very accurate classifiers (referred to as "weak" classifiers) into a classifier with high accuracy (a "strong" classifier) by upweighting the examples that the model is currently misclassfying.

• Binning

• Boosting

• Bagging

_________ is a synthetic feature that encodes nonlinearity in the feature space by multiplying two or more input features together.

• candidate sampling

• feature cross

• bucketing

• scaling

Features created by ______ or ______ alone are not considered synthetic features.

• normalizing

• bagging

• scaling

• boosting

In practice, machine learning models frequently cross continuous features.

• True

• False

Different cities in California have markedly different housing prices. Suppose you must create a model to predict housing prices. Which of the following sets of features or feature crosses could learn city-specific relationships between roomsPerPerson and housing price?

• One feature cross: [binned latitude X binned longitude X binned roomsPerPerson]

• Two feature crosses: [binned latitude X binned roomsPerPerson] and [binned longitude X binned roomsPerPerson]

• One feature cross: [latitude X longitude X roomsPerPerson]

• Three separate binned features: [binned latitude], [binned longitude], [binned roomsPerPerson]

If training loss gradually decreases, but validation loss eventually goes up. In other words, this generalization curve shows that the model is ______

• Underfitting

• Overfitting

• None

If your lambda value is too high, your model will be simple, but you run the risk of overfitting your data.

• True

• False

If your lambda value is too low, your model will be more complex, and you run the risk of overfitting your data.

• True

• False

________ means ending training before the model fully reaches convergence.

• regularization rate

• early stopping

• lambda

Imagine a linear model with 100 input features:

* 10 are highly informative.

* 90 are non-informative.

Assume that all features have values between -1 and 1.

Which of the following statements are true?

• L₂ regularization will encourage many of the non-informative weights to be nearly (but not exactly) 0.0.

• L₂ regularization will encourage most of the non-informative weights to be exactly 0.0.

• L₂ regularization may cause the model to learn a moderate weight for some non-informative features.