In traditional models like linear regression and ANOVA, assumptions such as linearity, independence of errors, homoscedasticity, and normality of residuals are foundational. These assumptions ...

Model-combining (i.e., mixing) methods have been proposed in recent years to deal with uncertainty in model selection. Even though advantages of model combining over model selection have been ...

We introduce a fast stepwise regression method, called the orthogonal greedy algorithm (OGA), that selects input variables to enter a p-dimensional linear regression model (with p ≫ n, the sample size ...

To illustrate the interaction between feature selection and linear regression, I scraped 500 rows of game logs from stats.nba.com, placed them in a .csv file on one of our test Hadoop clusters ...

I use Python 3 and Jupyter Notebooks to generate plots and equations with linear regression on Kaggle data. I checked the correlations and built a basic machine learning model with this dataset.

The first part of the demo output shows how a linear regression model is created and trained: Creating and training model Setting SGD lrnRate = 0.001 Setting SGD maxEpochs = 200 epoch = 0 MSE = 0.1095 ...