regression - What does it mean to regress a variable against …
Dec 4, 2014 · When we say, to regress $Y$ against $X$, do we mean that $X$ is the independent variable and Y the dependent variable? i.e. $Y =aX + b$.
regression - What are good RMSE values? - Cross Validated
Apr 17, 2013 · Suppose I have some dataset. I perform some regression on it. I have a separate test dataset. I test the regression on this set. Find the RMSE on the test data. How should I …
What is the relationship between R-squared and p-value in a …
Context - I'm performing OLS regression on a range of variables and am trying to develop the best explanatory functional form by producing a table containing the R-squared values …
python - regression with scikit-learn with multiple outputs, svr or …
May 25, 2015 · ValueError: Buffer has wrong number of dimensions (expected 1, got 2) Does anyone know how to deal with regression with multiple outputs in scikit-learn? Edit. I have …
Which pseudo-$R^2$ measure is the one to report for logistic …
The only assumptions made in logistic regression are that of linearity and additivity (+ independence). Although many global goodness-of-fit tests (like the Hosmer & Lemeshow …
Why are regression problems called "regression" problems?
I was just wondering why regression problems are called "regression" problems. What is the story behind the name? One definition for regression: "Relapse to a less perfect or developed state."
regression - Trying to understand the fitted vs residual plot?
Dec 23, 2016 · A good residual vs fitted plot has three characteristics: The residuals "bounce randomly" around the 0 line. This suggests that the assumption that the relationship is linear is …
Can I merge multiple linear regressions into one regression?
Oct 3, 2021 · Although one can compute a single regression for all data points, if you include model assumptions such as i.i.d. normal errors, the model for all points combined can't be …
Why use linear regression instead of average y per x
Mar 23, 2017 · Wow. So why bother going through the linear regression formulas if you can just divide the mean of y with the mean of x?
Why Isotonic Regression for Model Calibration?
Jan 27, 2025 · It appears that isotonic regression is a popular method to calibrate models. I understand that isotonic guarantees a monotonically increasing or decreasing fit. However, if …