๐จ๐ป๐ฑ๐ฒ๐ฟ๐๐๐ฎ๐ป๐ฑ๐ถ๐ป๐ด ๐๐ต๐ฒ ๐๐ถ๐ฎ๐-๐ฉ๐ฎ๐ฟ๐ถ๐ฎ๐ป๐ฐ๐ฒ ๐ง๐ฟ๐ฎ๐ฑ๐ฒ๐ผ๐ณ๐ณ ๐ถ๐ป ๐ฃ๐ผ๐น๐๐ป๐ผ๐บ๐ถ๐ฎ๐น ๐ฅ๐ฒ๐ด๐ฟ๐ฒ๐๐๐ถ๐ผ๐ป
When implementing linear regression, we often encounter scenarios where the model fails to achieve the desired Rยฒ score. This is usually because the data does not adhere to the linearity assumption. To address this, polynomial regression can be employed.
By introducing polynomial features into the dataset, the model is capable of fitting a curve instead of a straight line. The degree of the polynomial determines how much curvature the model can capture.
๐๐ถ๐ด๐ต-๐ฑ๐ฒ๐ด๐ฟ๐ฒ๐ฒ ๐ฝ๐ผ๐น๐๐ป๐ผ๐บ๐ถ๐ฎ๐น๐ provide high accuracy (low bias) but may lead to overfitting (high variance).
๐๐ผ๐-๐ฑ๐ฒ๐ด๐ฟ๐ฒ๐ฒ ๐ฝ๐ผ๐น๐๐ป๐ผ๐บ๐ถ๐ฎ๐น๐ ensure low variance but might result in underfitting (high bias).
Striking the right balance between bias and variance is crucialโthis is referred to as the Bias-Variance Tradeoff.
For a comprehensive understanding of this concept, check out this tutorial:
๐ฝ๏ธ ๐ฃ๐ผ๐น๐๐ป๐ผ๐บ๐ถ๐ฎ๐น ๐ฅ๐ฒ๐ด๐ฟ๐ฒ๐๐๐ถ๐ผ๐ป: Complete Tutorial | Adjusted Rยฒ | ๐๐ถ๐ฎ๐-๐ฉ๐ฎ๐ฟ๐ถ๐ฎ๐ป๐ฐ๐ฒ ๐ง๐ฟ๐ฎ๐ฑ๐ฒ๐ผ๐ณ๐ณ by Pritam Kudale:
I've also made the code for the animation in the video available for you to experiment with. You can find it here:
๐ป ๐ฃ๐ผ๐น๐๐ป๐ผ๐บ๐ถ๐ฎ๐น ๐ฅ๐ฒ๐ด๐ฟ๐ฒ๐๐๐ถ๐ผ๐ป ๐๐ป๐ถ๐บ๐ฎ๐๐ถ๐ผ๐ป ๐๐ผ๐ฑ๐ฒ: click here