Model Classifications

Question 1

Some examples of parametric models

Answer 1

• Linear Regression
• Logistic Regression
• Naive Bayes
• Simple Neural Networks

Question 2

Some examples of Non-Parametric models are:

Answer 2

• K Nearest Neighbors
• Support Vector Machines
• Decision Trees

Question 3

Def parametric model

Answer 3

A learning model that summarizes data with a set of parameters of fixed size. Specifically, a parametric model is a family of probability distributions that has a finite number of parameters.

Question 4

Strengths of Parametric Models?

Answer 4

- Simpler: These methods are easier to
understand and the results are easy to
interpret.
- Speed: Parametric models are usually
very fast to train.
- Less Data: They do not require as much
training data and can work well even if
the fit to the data is not perfect.

Question 5

Weaknesses of Parametric Models?

Answer 5

-Constrained: By choosing a functional
form, these methods are highly
constrained to the specified form.
-Limited Complexity: The methods are
more suited to simpler problems.
-Poor Fit: In practice, the methods may
not always match the underlying
mapping function.

Question 6

Def non-parametric model

Answer 6

A learning model that does not make strong
assumptions about the form of the mapping function. By not making assumptions, ____________ are free to learn
any functional form from the training data.

Question 7

Strengths of non-parametric models

Answer 7

- Flexibility: Capable of fitting a large
number of functional forms.
- Power: No assumptions (or weak
assumptions) about the underlying
function.
- Performance: Can result in higher
performance models for prediction.

Question 8

Weaknesses of non-parametric models

Answer 8

- More data: Require a lot more training
data to estimate the mapping
function.
- Slower: A lot slower to train, as they
often have far more parameters to
train.
- Overfitting: Have a higher risk of
overfitting against the training data

Question 9

Lazy or instance-based learners or

rote learners

Answer 9

A class of non-parametric learning methods that do
not generate a model but instead make use of
verbatim training data for classification.

Question 10

An individual independent example of the concept
represented by the dataset. It is described by a set of
attributes or features

Answer 10

Instance / row / observation

Question 11

Property or characteristic of an instance. These can

either be discrete or continuous.

Answer 11

Feature / variable

Question 12

The attribute or feature that is described by the other

features within an instance.

Answer 12

Class / label

Question 13

The ___________ of a dataset represents the number of features in the dataset.

Answer 13

dimensionality

Question 14

Data _______ and _______ describe
the degree to which data exists for
each feature of all observations.

Answer 14

Density / Sparsity

Question 15

_________ is the process
of reducing noise in the
data.

This can be done by:

• __________
• __________
• __________

Answer 15

Smoothing
Binning
Clustering
Regression