A Cumulative Gain Calculator is used to determine the total gain accumulated over multiple periods or segments. This is particularly useful in finance, business analytics, machine learning, and investment tracking to measure the overall performance of a strategy over time.
Cumulative gain is often used in predictive modeling and ranking evaluation, where it helps assess the effectiveness of classification models. In finance, it allows investors to track profit growth over multiple transactions or time periods.
By summing up individual gains, this calculator provides a simple yet powerful tool for tracking performance and making informed decisions.
Formula of Cumulative Gain Calculator
The cumulative gain is calculated using the following formula:
Cumulative Gain = Gain1 + Gain2 + Gain3 + … + GainN
Where:
- Gain1, Gain2, Gain3, …, GainN are the individual gains recorded for each period or transaction.
- N is the number of periods or data points.
This formula allows users to track total gains over multiple periods.
Pre-Calculated Cumulative Gain Table
For quick reference, here is a table with cumulative gains over different periods:
| Period | Gain (%) | Cumulative Gain (%) |
|---|---|---|
| Day 1 | 2.5 | 2.5 |
| Day 2 | 3.0 | 5.5 |
| Day 3 | 1.8 | 7.3 |
| Day 4 | 4.2 | 11.5 |
| Day 5 | 2.1 | 13.6 |
This table provides an overview of how cumulative gains add up over time.
Example of Cumulative Gain Calculator
Let’s calculate cumulative gain for an investment over five days, assuming the following daily gains:
- Day 1: 1.5%
- Day 2: 2.0%
- Day 3: 1.8%
- Day 4: 2.5%
- Day 5: 1.2%
Using the formula:
Cumulative Gain = 1.5 + 2.0 + 1.8 + 2.5 + 1.2
Cumulative Gain = 9.0%
So, over five days, the total cumulative gain is 9.0%.
Most Common FAQs
Cumulative gain provides a clear picture of overall growth, whether in investments, business performance, or machine learning models. It helps assess long-term trends rather than individual short-term gains.
In machine learning, Cumulative Gain (CG) curves help evaluate ranking models, showing how well a model predicts relevant outcomes compared to a random baseline.
Yes, if losses are greater than gains, cumulative gain can become negative, indicating overall decline instead of growth.