Linear Regression In Azure ML

Ginger Grant gives a brief discussion of linear regression:

There are two types of indicators for linear correlation, positive and negative as shown on the following charts. The Y axis represents Grades, and the x axis is changed to show positive and negative correlation of the amount of X on grades. When X is the amount of study hours, there is a positive correlation and the line goes up. When X is changed to watching cat videos, there is a negative correlation. If you can’t draw a line around the points there is no correlation. If I were to create a graph where X indicated the quantity of the bags of Cheese Doodles consumed on grades, it would not be possible to draw a straight line, where the data points cluster around it. Since this is Line-ar regression, if that line doesn’t exist there is no correlation. Knowing there is no correlation is also useful.

Simple linear regression is a powerful tool and gets you to “good enough” more frequently than you’d think.

Related Posts

Priority Queuing In Azure SQL Data Warehouse

Matt How walks us through an improvement to Azure SQL Data Warehouse: The concept of workload management is a key factor for Azure SQL DW as there is only limited concurrency slots available and depending on the resource class, these slots can fill up pretty quickly. Once the concurrency slots are full, queries are queued […]

Read More

Bias Correction In Standard Deviation Estimates

John Mount explains how to perform bias correction and explains why it happens so rarely in practice: The bias in question is falling off at a rate of 1/n (where n is our sample size). So the bias issue loses what little gravity it ever may have ever had when working with big data. Most sources of noise will […]

Read More

Categories

April 2016
MTWTFSS
« Mar May »
 123
45678910
11121314151617
18192021222324
252627282930