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How do you measure lift and confidence in rule mining?

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Rule mining is a data mining technique that discovers patterns and associations among items or variables in a dataset. For example, you might use rule mining to find out what products are frequently bought together, or what factors influence customer satisfaction. But how do you measure the quality and relevance of the rules you find? Two common metrics are lift and confidence. In this article, you will learn what they are, how they are calculated, and how they can help you evaluate and compare rules.

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Calculating confidence:

To measure how often a rule proves true, divide the number of transactions with both antecedent and consequent by those with only the antecedent. This percentage will tell you how reliable your rule is.
Question associations:

Remember, seeing two things together doesn't mean one causes the other. Dig deeper for external proof before drawing conclusions from your data to ensure you're making informed decisions.

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Ricardo Fitas

PhD Candidate | MSc Mechanical…
Khushboo Rai

Data Analyst

1 What is confidence?

Confidence is a measure of how often a rule is true in the data. It is the ratio of the number of transactions that contain both the antecedent and the consequent of the rule, to the number of transactions that contain only the antecedent. For example, if you have a rule that says {bread, butter} => {jam}, the confidence is the proportion of transactions that have bread, butter, and jam, out of all the transactions that have bread and butter. A high confidence means that the rule is reliable and consistent.

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Khushboo Rai

Data Analyst
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Confidence quantifies the frequency of a rule's validity within the data. It's computed by comparing the number of transactions featuring both the antecedent and the consequent of the rule to those containing solely the antecedent. For instance, consider the association rule {coffee, newspaper} => {morning}. In this rule, "morning" is the consequent, indicating that it's what you expect to occur given the presence of "coffee" and "newspaper." So, if you find high confidence, the association suggests that the presence of coffee and newspaper is strongly linked to the morning time.

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SUDHENDU K.

Electrical Engineer
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Confidence is a measure of the strength of the association between two items. It is calculated as the number of transactions containing both items divided by the number of transactions containing the first item. High confidence indicates that the presence of the first item is a strong predictor of the presence of the second item.

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2 What is lift?

Lift is a measure of how much more likely the consequent of a rule is to occur when the antecedent is present, compared to when it is absent. It is the ratio of the confidence of the rule, to the frequency of the consequent in the whole dataset. For example, if you have a rule that says {bread, butter} => {jam}, the lift is the confidence of the rule, divided by the proportion of transactions that have jam. A high lift means that the rule is significant and interesting, and that there is a strong association between the antecedent and the consequent.

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SUDHENDU K.

Electrical Engineer
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Lift is a measure of the strength of the association between two items, taking into account the frequency of both items in the dataset. It is calculated as the confidence of the association divided by the support of the second item. Lift is used to compare the strength of the association between two items to the expected strength of the association if the items were independent. A lift value greater than 1 indicates that the association between two items is stronger than expected based on the frequency of the individual items. This suggests that the association may be meaningful and worth further investigation. A lift value less than 1 indicates that the association is weaker than expected and may be less reliable or less significant.

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3 How to calculate confidence and lift?

In order to calculate confidence and lift, you need to know the support, antecedent support, and consequent support values. The formulas for these are confidence = support / antecedent support and lift = confidence / consequent support. To illustrate, if you have a dataset of 100 transactions with 10 of them having bread, butter, and jam; 20 of them having bread and butter; and 15 of them having jam, then the support would be 0.1 (10 / 100), antecedent support would be 0.2 (20 / 100), consequent support would be 0.15 (15 / 100), confidence would be 0.5 (0.1 / 0.2), and lift would be 3.33 (0.5 / 0.15).

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4 How to interpret confidence and lift?

Confidence and lift can help you evaluate and compare rules based on their strength and relevance, but they have some limitations and trade-offs to be aware of. Confidence only reflects the direction of the rule, not the reverse; it may be misleading if the consequent is very frequent or rare in the data. Lift takes into account the frequency of the consequent in the data, and adjusts confidence accordingly. It can also be used to rank rules based on their interestingness and novelty, but can be misleading if the antecedent or consequent is very frequent or rare.

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5 How to use confidence and lift in practice?

Confidence and lift are useful metrics for rule mining, but they should be used as indicators, not criteria. Other factors to consider when selecting the best rules include the size and quality of the data, domain knowledge and business goals, the trade-off between accuracy and coverage, and the trade-off between simplicity and complexity. Additionally, other metrics such as leverage, conviction, interest, or correlation can complement confidence and lift. Visualization tools like scatter plots, heat maps, or association graphs can help to compare and contrast rules based on multiple dimensions. Ultimately, rule mining is a powerful data mining technique that can reveal hidden patterns and insights in your data. By considering various factors and metrics when selecting rules, you can effectively assess their quality and relevance to ensure the best results for your analysis or application.

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6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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Ricardo Fitas

PhD Candidate | MSc Mechanical Engineering | Innovating with AI & Numerical Optimization
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Observing an association does not mean there is a direct causal relationship. It's important to question the reasons behind an association and seek external validations to avoid making misguided decisions. Always approach rules with a discerning eye and remember that correlation does not imply causation.

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How do you measure lift and confidence in rule mining?

1

2

3

4

5

6

1 What is confidence?

2 What is lift?

3 How to calculate confidence and lift?

4 How to interpret confidence and lift?

5 How to use confidence and lift in practice?

6 Here’s what else to consider

Data Mining

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How do you measure lift and confidence in rule mining?

1

2

3

4

5

6

1 What is confidence?

2 What is lift?

3 How to calculate confidence and lift?

4 How to interpret confidence and lift?

5 How to use confidence and lift in practice?

6 Here’s what else to consider

Data Mining

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