Mistakes Naïve Machine Learning Engineers Make

Mistakes Naïve Machine Learning Engineers Make

Whenever you are working on something, you are bound to make mistakes. If you are new at something or still learning, it is obvious that you will make a few errors along the way. But, that’s okay. As long as you learn from them and move ahead, making mistakes is quite alright.

Now, that applies to people who are new to a field or do not have adequate knowledge about it. Today, we are going to see some of the common mistakes that naive machine learning engineers make so that we can learn from them, and more importantly, avoid them in the future.

One Shoe Fits All

When you learned data structures and algorithms, you learned many algorithms that can be used. You also learned to develop some algorithms for specific problems. If you’ve done data analyst or analytics courses online you will know that every algorithm has its own different purpose. Now as machine learning engineers, one of the most common mistakes people make is using one algorithm for all cases.

If you are using the same model for everything, you are not getting the best results. You need to try some algorithms and then find out which one works best for your problem at hand. If you find yourself using the same algorithm all the time, then you are doing something wrong.

Not Dealing Properly with Cyclic Features

What are cyclic features? Now things like days of a week, or hours in a day count as cyclic features. But, people who are new to the field or do not have adequate knowledge about it tend to mess it up.

They do not think about representing them in such a way that their cyclic nature is also evident in the representation. For example, when they are representing hours in a day, they should be showing 23 hrs and 0 hrs consecutively. Instead of taking their face value and keeping them apart, they should be represented in such a way that you are actually displaying the working of a clock.

Not Looking at the Data

Machine learning is based heavily on data. That is the primary and the most important resource you are working on. Your algorithms and all the operations you are going to perform will happen on this data. Almost every data analyst course or analytics courses you will find online and offline explains the importance of data.

But, some engineers make the mistake of focusing only on algorithms and so on. If you’ve read the data wrong or carelessly, it won’t matter if you have chosen the best model or not, because you will still not get the right results.

Not Comparing to a Simple Model

Every problem does not have to have a complicated solution. A lot of machine learning engineers today are trying to make models that are complicated. Many a time, there is no need to have such a complicated model, the best solution can lie in the simpler versions.

Instead of going for complicated solutions even before seeing if there is a simpler one possible, will not give you the best possible results. If you find yourself doing this, then you can try to look for simpler models first, and if they don’t work, then you can go for the more complicated ones.

Not Using Existing Solutions

You do not have to create a model for every single problem you get along the way. As a machine learning engineer, you are going to be tackling many questions along the way. The chances are that there may already exist a solution for a specific part of the problem as people have been using data structures and algorithms for a while now. So, instead of creating a new one every time, you can use the existing one and then build on it.

This will save you a lot of time, and you can actually focus on the problem that is important.

Creating Something Just for the Sake of it

Nowadays we can see that many people are creating solutions for problems that did not exist. There is a lot of progress in the field of data structures and algorithms, which is a very positive sign. However, there are many solutions that exist just for the sake of it. They did not have any real applications, but yet they exist.

Now, once you are a machine learning engineer, you need to focus on the problems at hand. If you feel like there is a need for something, then you need to do your research on it and see if it is actually relevant today or not. Check to see if people really need it and then start working on it.

Now, these are some of the common mistakes naive machine engineers tend to make. Now that we have listed them down, it will be easier for us to learn from them and avoid them in the future.

You can opt for some of the data analyst course or any of the analytics courses you can find online so that you can get a better understanding and avoid such errors.