Chapter 1: Machine Learning for Predictive Analysis Flashcards

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1
Q

What is the job of data analytics?

A

Extracting insights from data

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2
Q

What is predictive data analytics?

A

The art of building and using models that make predictions based on patterns extracted from historical data

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3
Q

What are the applications of predictive data analysis?

A

price prediction (businesses), dosage prediction (doctors), risk assessment (organizations), propensity modeling (predicting the likelihood or propensity of individuals to take different actions), diagnosis (doctors, engineers and scientists), document classification

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4
Q

What is a prediction in data analytics? How is it different from the everyday usage?

A

In DA a prediction is the assignment of a value to any unknown variable. In everyday usage it has a temporal aspect, we predict what will happen in the future

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5
Q

What two things are common in all the application examples?

A

in each case, a model is used to make a prediction to help make a decision AND a model is trained to make predictions based on a set of historical examples (machine learning is used to train these models)

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6
Q

What is machine learning?

A

Machine learning is an automated process that extracts patterns from data.

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7
Q

What is supervised machine learning used for?

A

We use supervised machine learning to build the models used in predictive data analytics applications
- They have labels/ classes/ events that provide us with feedback while learning

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8
Q

How do they work?

A

They automatically learn a model of the relationship between a set of descriptive features and a target feature based on a set of historical examples (instances)

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9
Q

What is each row of a dataset called?

A

training instance

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10
Q

What is the overall dataset called?

A

training dataset

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11
Q

When is a model consistent?

A

when there are no instances in the dataset for which the model does not make a correct prediction

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12
Q

What do machine learning algorithms do?

A

automate the process of learning a model that captures
the relationship between the descriptive features and the target feature in a dataset.

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13
Q

Why is searching for consistent models not enough to learn useful prediction models?

A
  • When dealing with large databases there will likely be noise
  • The training set represents only a small sample of the possible set of instances in the domain.
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14
Q

What is an ill-posed problem?

A

An ill-posed problem is a problem for which a
unique solution cannot be determined using only the information that is available

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15
Q

Why is machine learning an ill-posed problem?

A

A single consistent model cannot be found based on the sample training dataset alone

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16
Q

What is generalization?

A
  • The ability to make predictions for queries that are not present in the data
  • A prediction model that makes the correct predictions for these
    queries captures the underlying relationship between the descriptive and target features
    and is said to generalize well
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17
Q

What is the goal of machine learning?

A

Finding the predictive model that generalizes best

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18
Q

What is inductive bias?

A

A set of assumptions that defines the model selection criteria of a machine learning algorithm

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19
Q

What are the types of inductive bias?

A
  • Restriction bias
    -Preference bias
20
Q

What is restriction bias?

A
  • It constrains the set of models that the algorithm will
    consider during the learning process
  • Similar to choosing your go-to study method
  • Tells us what our model is able to represent
21
Q

What is preference bias?

A

-It guides the learning algorithm to
prefer certain models over others
- Choosing out convergence/satisfaction mechanism
- I like group study but prefer to be the leader than weakest link
- Algorithm’s belief about what makes a good hypothesis

22
Q

What are examples of restriction bias?-

A
  • In multivariable linear regression with gradient descent we only consider models that produce description based on a linear combination of the descriptive features
  • In Iterative Dichotomizer 3 we only consider tree-like prediction models where each branch encodes a sequence of checks on individual descriptive features
23
Q

What are examples of preference bias?

A
  • In MLR with GD we linearly combine the descriptive features using only weights that were found though our gradient descent approach
  • In ID3 we are preferring shallower (less complex) trees over larger/deeper trees
24
Q

Why is inductive bias necessary for learning beyond the dataset?

A

Without it we could only perform memorization of our training dataset without generalization capacity

25
Q

What is model induction?

A

The creation of models from data

26
Q

What is the difference between classification problem and regression problem?

A
  • Classification problem has the target as a category
  • Regression problem has the target as a number
27
Q

What is another name for dataset?

A

-One whose form is the same is a table/relation of a database
- Worksheet of a spreadsheet
- Array in math

28
Q

What is an instance

A
  • Row, tuple, record of a database table
  • Case in statistics
  • Object of a class in programming
  • Datapoint or vector in math
29
Q

What is an independent variable?

A
  • It is the attribute supplied as input
  • Also known as explanatory variable, inputs, predictors
    -Features are the table’s columns
30
Q

What is dependent variable

A

The target variable whose values are to be predicted.
Aka class or label or output

31
Q

What are some confusing facts about independent and dependent variables?

A
  • Independent variables may not be independent on each other or anything else
  • Dependent variables does not always depend of all the independent variables
32
Q

Facts about the target variable

A
  • Sometimes it is considered to be included in the set of features, sometimes it is not
  • The target variable is not used to predict itself
  • Prior values may be helpful to predict future values and may be included as input features
33
Q

What is the process of building a model (or training your classifier) from historical data?

A

Induction, learning, training or generalization

34
Q

When does the real value of machine learning become apparent?

A

When we want to build prediction models from large datasets with multiple features

35
Q

How do you know the number of possible prediction models?

A
  • There are three descriptive features so there are 2^3 possible combinations of descriptive feature values
  • For each descriptive feature there are 3 possible target feature values
  • There are 3^8 = 6,561 possible prediction models
36
Q

What is the ability to memorize a training dataset?

A

Consistency

37
Q

What does Occam’s Razor say about simplicity?

A

With all things being equal the simplest explanation tends to be the right one (upper bound)

38
Q

What does albert Einstein say about simplicity

A

Everything should be made as simple as possible but not simpler (lower bound)

39
Q

What are the sources of information that guide machine learning algorithms?

A
  • Training data
  • Inductive bias of the algorithm
40
Q

What can go wrong with machine learning?

A
  • Inappropriate inductive bias which leads to mistakes
41
Q

What does no free lunch mean?

A

if an algorithm does well on a certain class of problems then it necessarily pays for that with degraded performance on the set of all remaining problems

42
Q

What happens if we choose the wrong inductive bias?

A
  • Underfitting (the prediction model is oversimplifies)
    -Overfitting (the prediction model is so complex that it becomes too sensitive to noise in the data, it memorizes)
43
Q

What is a Goldilocks model?

A
  • A model that is just right and strikes a good balance between overfitting and underfitting
  • it is found by using algorithms with appropriate inductive biases
44
Q

What is CRISP-DM

A

Cross Industry Standard Process for Data Mining is a data mining process model that describes commonly used approaches that data mining experts use to tackle problems

45
Q

What are the phases?

A

-Business Understanding- defining customers’ needs, understanding project objectives
-Data Understanding- collection and data familiarity
-Data Preparation- construct final dataset from raw data
-Modeling- select machine learning techniques relevant to the problems and their parameters are calibrated to optimal values
-Evaluation- outcome collection, compare obtained model with business objectives
-Deployment- put into production, organize and present knowledge gain in a way that the customer can use it

46
Q

List other data life cycle models

A

-Semma- Sample, Explore, Modify, Model, Assess
- Data Mining and Knowledge Discovery from Data (KDD)- mostly used in the real world

47
Q

What is supervised machine learning based on?

A

The assumption that data does not change over time. They create models that distinguish between classes present in the dataset they are induced from