This article describes the analytical technique of gradient boosting regression.
What is Gradient Boosting Regression?
Gradient Boosting Regression is an analytical technique that is designed to explore the relationship between two or more variables (X, and Y). Its analytical output identifies important factors ( Xi ) impacting the dependent variable (y) and the nature of the relationship between each of these factors and the dependent variable.
Gradient Boosting Regression is limited to predicting numeric output so the dependent variable has to be numeric in nature. The minimum sample size is 20 cases per independent variable.
To understand Gradient Boosting Regression, let’s look at a sample analysis to determine the quality of a diamond:
How Can Gradient Boosting Regression Be Helpful for Your Enterprise?
If we consider the use cases below, we can see the value of Gradient Boosting Regression.
Business Use Case – eCommerce
Business Problem: An eCommerce business wishes to measure the impact on product sales by product price, product promotions during a festival or season.
Input Data: Predictor/Independent Variable(s)
- Product price
- Product promotions and discounts
- Dates fall within or outside Season/Festival
Dependent Variable: Product Sales Data
- Sales Managers can analyze which of the Predictors included in the analysis will have significant impact on product sales
- Targeted sales strategies will include consideration of appropriate predictors to ensure accuracy
- If promotions and seasons/festivals are significant factors, with a positive coefficient, these factors can be included in a marketing strategy to improve sales
Business Use Case – Agriculture
Business Problem: An agriculture production business wishes to predict the impact of the amount of rainfall, humidity, temperature etc. on the yield of a particular crop.
Input Data: Predictor/Independent Variables
- Amount of rainfall during monsoon months
- Humidity levels/measurements
- Temperature measurements
Dependent Variable: Crop production
- The business can understand the impact of each predictor on the target variable
- If temperature and rainfall have a positive significant impact but humidity has a negative significant impact on crop yield it can adjust crop production to accommodate high temperature and rainfall levels and low humidity levels to produce the desired crop yield.
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