# Feature scaling strategy - Mean, Median or Mode?

_{Posted on March 25, 2020 - 4 min read}

### What is feature scaling

Take this list of numbers for example, what do you see here?

`89,90,91,93,95`

- The numbers are gradually increasing
- They’re pretty close to each other
- Since they’re pretty close to each other, their euclidian distance will be minimal
- A low euclidian distance would mean that your inference/classification algorithms (that use distance as a metric) can easily make decisions on this list of numbers.

But obviously, in real life, you neither run inference/classification on such a simple list of numbers, nor your data will be this gracefully ranged. You, dear padawan, have to range it yourself. In technical terms, you have to scale, or normalize your data within a certain range so that your algorithm doesn’t have to go on a 10 hour Batman Seizure.

This process of scaling your data is known as **feature scaling**, also called **normalization**. But why do we care about Mean, Median or Mode while normalizing data?

### The notion of central tendency

Central Tendency is what gives a bird eye view of your data, also some insights into what you can expect from it. Mean, Median, Mode all are metrics of central tendency and it depends on how your data is spread over or ranged or distributed that which one these is going to give you better insight into your data.

Consider the previous list of numbers again. If we calculate the mean of these numbers, we get,

$mean = \frac{89 + 90 + 91 + 93 + 95}{5} = 91.6$91.6 is pretty close to all the numbers in the list, so when you go onto make decisions about this list you can say that the numbers are around 91.6 and so on. In this case, Mean does a pretty good job at telling you what to expect. Now how about this list of numbers?

`3, 3, 3, 3, 100`

22.4 is neither close to 3 or 100. Mean isn’t helping us here. However if we take the Median or Mode, which is 3, we get a pretty clear picture about this list of numbers. Also, based on the distribution, we can call 100 an outlier here (something that’s out of pattern).

### Effect of central tendency on feature scaling

When you’re about to normalize your dataset, your goal should be to center your data as much as you can around the central tendency. Take a look at this illustration or a Normal / Gaussian Distribution

At the center you’ve the mean and the distribution is spread at the range 3 on both sides of the mean. Here the central tendency is the mean, and from the standard deviation you can measure how far from the mean the data is going to be. So, when you normalize your data, it should follow the same principle. Not all datasets will follow a normal distribution but you can convert them to follow normal distribution by applying a tiny bit of high school statistics.

### So which one then

Now, what form of central tendency to choose while scaling features? It depends entirely on your data! Just because you can normalize data doesn’t mean that every dataset out there will play nice with the normal distribution. Some datasets can be erratic by nature and may not adhere to normal distribution at all. In such cases you’ve to carefully study your data and decide on which one to choose. You can also go on a trial and error fest to check which form or central tendency gives you better results.

[N.B. - how to normalize a dataset or showing some fancy python code wasn’t the intent of this post. You can just use equations from a Statistics book or use wonderful mathematical or machine learning libraries out there do ease your job. Code isn’t always important. Knowing what to use when is.]