A demonstration is one historical data record containing independent variables and the corresponding target variable, also called the label. Together, such records form labeled training data for supervised learning, where learned patterns support predictions on unseen future data. section 8.3.1 section 1.5.1
A demonstration connects three practical elements:
- Input variables describe the observed attributes used to make a prediction. section 8.3.1
- The target variable records the desired outcome, such as revenue, weight, or a churn decision. section 1.5.1
- Many demonstrations allow supervised algorithms to learn patterns for regression or classification. section 1.5.1
Telecom churn record
One telecom customer record can contain revenue, service duration, average cost, and monthly usage as inputs, with Churned (Y/N) as its target. Each row is a demonstration for predicting churn in future records. section 1.5.1
Table 1.3 Example of a structured dataset for a telecom operator showing multiple data attributes
|
ID
|
Revenue ($)
|
Duration of service (years)
|
Avg. cost
|
Monthly usage (days)
|
Churned (Y/N)
|
|---|---|---|---|---|---|
|
1001
|
100
|
1.1
|
0.10
|
10
|
Y
|
|
1002
|
200
|
4.1
|
0.09
|
25
|
N
|
|
1003
|
300
|
5.2
|
0.05
|
28
|
N
|
|
1004
|
200
|
0.9
|
0.25
|
11
|
Y
|
|
1005
|
100
|
0.5
|
0.45
|
12
|
Y
|
Inspect a structured telecom dataset showing how several input attributes and one target value occupy each historical record. section 1.5.1
