When you start to learn linear regression and Logistic regression you will come up with an equation. The equation will contain theta. You might be wondering what does this theta in machine learning mean.
A few days ago I was in my EMBA class “Managing Operations”. our class topic was forcasting. In that class I learned something about weights. Later I was able to relate between weights and theta. Today I will explain what is theta in terms of weights.
Suppose you have an online store where you sell expensive, unique and stylus pens. In last year you advertised on Google AdWords, Facebook, Twitter and a local newspaper. From last year’s data you have come to know that Google advertisement was more effective than all other advertisements. This year you have to make a plan for advertising on different media. To get a better result what will you do?
Simple answer: You will spend more money on Google advertisement. From your previous year’s data you have found that Facebook is in second position. So your second priority will be Facebook advertisement. You have advertised on your local newspaper too. However, you couldn’t figure out how much traffic did you get from that advertisement. So this year you are not going to advertise on local newspaper at all.
Let’s assume, your this year’s total advertisement budget is US$ 6000. We can write down your this year’s advertisement plan as follows:
$3000 Google advertisement + $2000 Facebook advertisement + $1000 twitter advertisement + $0 Local newspaper advertisement = $6000.
From above calculation we can say that we are spending most of the money in Google advertisement. Alternatively, we can say most weight goes to Google advertisement. So by using weights you give different priority to different media.
Now you understood what does it mean by weight. If so then you also understand the meaning of theta in machine learning. In machine learning algorithms we use theta in different features to give them different priority or weight based on their importance.
House price example
When we do linear regression and Logistic regression we use some features. For example, suppose we are going to find out house price of any specific area. For that we take some historical value or previous selling price data. From that data we take different features of house. e.g. number of rooms, number of bathrooms, kitchens and whether the house is beside the main road or far from the main road.
For calculating house price we give more priority to some features and less priority to some other features. For example, number of rooms have more weight or priority than number of kitchens. If there are 5 kitchens in a 2000 square feet home, that may not add more value to the house. Because only one or two kitchens are enough. That’s why number of kitchens are not major issues in predicting house price. So we give less weight to kitchen and more weight to rooms as well as total area of the house.
So now if you see a machine learning algorithm with theta, you will be able to figure out what does it mean.