Quadratic Model, Multiple Regression & Overfitting.

Quadratic model is used when the relationship between independent and dependent variable isn’t a linear or it’s scatterplot is in U shape where we say quadratic regression model best firs for the given data. So, this is a way to figure out equation of parabola.

To derive more deeper relationships we use the multiple regression model which gives predictions.

Overfitting encountered when the model gets close to training data & becomes unpredictable for new data. This overfit encountered when machine learning model is small & training dataset is extremely small/large for model complexity. To avoid this overfitting we don’t have particularly a specific technique but there are few various techniques like Cross-validation. This evaluates machine learning algorithm performance to how far does it make predictions on data set that not been trained. Here we’re using R squared value is a measure that is calculated how good model fits. If it’s value is 1 it implies that the model fits perfectly  and 0 implies model doesn’t fit.