WEEK 10: Implement the non-parametric Locally Weighted Regression algorithm in order to fit data points. Select appropriate data set for your experiment and draw graphs.
WEEK 10: Implement the non-parametric Locally Weighted Regression algorithm in order to fit data points. Select appropriate data set for your experiment and draw graphs. Locally Weighted Regression Algorithm Regression: · Regression is a technique from statistics that are used to predict values of the desired target quantity when the target quantity is continuous. o In regression, we seek to identify (or estimate) a continuous variable y associated with a given input vector x. § y is called the dependent variable. § x is called the independent variable. Loess/Lowess Regression: Loess regression is a nonparametric technique that uses local weighted regression to fit a smooth curve through points in a scatter plot. Lowess Algorithm: · Locally weighted regression is a very powerful nonparametric model used in statistical learning. · Given a dataset X, y, we attempt to find a model parameter β(x) that minimizes residual sum of weighted squared error