Understanding gradient boosting techniques
To improve the performance of an algorithm, we can perform a series of steps and use different techniques, depending on the type of algorithm and the specific problems being addressed. The first approach involves a thorough analysis of the data to identify possible inaccuracies or shortcomings. In addition, many algorithms have parameters that can be adjusted to achieve better performance – not to mention techniques such as feature scaling or feature selection. A popular technique is to combine the capabilities offered by different algorithms to achieve better overall performance.
Approaching ensemble learning
The concept of ensemble learning involves the use of multiple models combined in a way that maximizes performance by exploiting their strengths and mitigating their relative weaknesses. These ensemble learning methods are based on weak learning models that do not achieve high levels of accuracy on their own, but when combined...
