Jul 23, 2016 · When the differences from predicted and actuals are large the log function helps normalizing this. By applying logarithms to both prediction and actual numbers, we’ll get smoother results by reducing the impact of larger x, while emphasize of smaller x. Mean absolute error; Mean absolute percentage error; Mean squared error; Root mean squared error; This article includes a list of references, related reading or external links, but its sources remain unclear because it lacks inline citations. Please help to improve this article by introducing more precise citations.For line and polygon features, feature centroids are used in distance computations. For multipoints, polylines, or polygons with multiple parts, the centroid is computed using the weighted mean center of all feature parts. The weighting for point features is 1, for line features is length, and for polygon features is area. Python 3 users should then run 2to3-w. from inside this directory so as to automatically adapt the code to Python 3. Source code ¶ The latest, bleeding-edge but working code and documentation source are available on GitHub . Using the Median Absolute Deviation to Find Outliers. Written by Peter Rosenmai on 25 Nov 2013. Last revised 13 Jan 2013. One of the commonest ways of finding outliers in one-dimensional data is to mark as a potential outlier any point that is more than two standard deviations, say, from the mean (I am referring to sample means and standard deviations here and in what follows). Apr 06, 2019 · Simple linear regression lives up to its name: it is a very straightforward approach for predicting a quantitative response Y on the basis of a single predictor variable X. It assumes that there is approximately a linear relationship between X and Y. Mathematically, we can write this linear relationship as $$ Y ≈ β_{0} + […]