Hello,
Greetings!
Today we explored the concept of performing regression analysis using two variables.
Y=β0+β1X1+β2X2+……+βpXp+ϵ
Where, Y = Diabetics dataset
X1 = Inactivity dataset
X2 = Obesity dataset
If the independent variable has a parabolic shape on the dependent variable, then a quadratic model is used.
For instance, if the volume of a certain chemical affects the quality of a product, also a quadratic model would suggest that there is an optimal volume that maximizes quality. A chemical that is too little or too much will lower quality.
Overfitting occurs when a predictive model is too complex and overfits the sample data too closely. This can lead to poor performance on new data.
For instance, when predicting stock prices, an overfitting model could fit the sample data with less volume closely, capturing short-term fluctuations, but it may struggle to provide accurate forecasts for future stock prices.
In my next blog, I’ll be posting about cross-validation for assessing the efficacy of the model.