6 Ways to Improve Your ML Model Accuracy
Simple steps for much better results
One of the most frustrating things that happen — more often than data scientists like to admit — after they spend hours upon hours gathering data, cleaning it, labeling it, and using it to train and develop a machine learning model is ending up with a model with low accuracy or large error range.
In machine learning, the term model accuracy refers to the measurements made to decide whether or not a certain model is the best to describe the relationship between the different problem variables. We often use training data (sample data) to train a model for new, unused data.
If our model has good accuracy, it will perform well on both the training data and the new one. Having a model with high accuracy is essential to the overall project’s success, and if you’re building it for a client, it’s important for your paycheck!
From a business perspective, performance equals money; if a model’s accuracy is low, it will result in more errors, which can be very costly. And I am not just talking about the financial aspect; imagine a model used to diagnose cancer or any other terminal diseases; a wrong diagnosis will not only cost the hospital money but will cost the patient and their family unnecessary emotional trauma.5 Types of Machine Learning Algorithms You Need to KnowIf you’re new to data science, here’s a good place to starttowardsdatascience.com
So, have can we avoid all of that and improve the accuracy of our machine learning model? There are different ways a data scientist can use to improve their model’s accuracy; in this article, we will go through 6 of such ways. Let’s jump right in…
Most ML engineers are familiar with the quote, “Garbage in, garbage out”. Your model can perform only so much when the data it is trained upon is poorly representative of the actual scenario. What do I mean by ‘representative’? It refers to how well the training data population mimics the target population; the proportions of the different classes, or the point estimates (like mean, or median), and the variability (like variance, standard deviation, or interquartile range) of the training and target populations.
Generally, the larger the data, the more likely it is to be representative of the target population to which you want to generalize. if you want to generalize the population of students in Grade 1 to 12 of a school you cannot just use 80% of Grade 8 population because the data you want to predict will be faulty because of your dataset. It is crucial to have a good understanding of the distribution of your target population in order to devise the right data collection techniques. Once you have the data, study the data (the exploratory data analysis phase) in order to determine its distribution and representativeness.
Outliers, missing values, and outright wrong or false data are some of the other considerations that you might have. Should you cap outliers at a certain value? Or remove them entirely? How about normalizing the values? Should you include data with some missing values? Or use the mean or median values instead to replace the missing values? Does the data collection method support the integrity of the data? These are some of the questions that you must evaluate before thinking about the model. Data cleaning is probably the most important step after data collection.
Method 1: Add more data samples
Data tells a story only if you have enough of it. Every data sample provides some input and perspective to your data’s overall story is trying to tell you. Perhaps the easiest and most straightforward way to improve your model’s performance and increase its accuracy is to add more data samples to the training data.
Doing so will add more details to your data and finetune your model resulting in a more accurate performance. Rember after all, the more information you give your model, the more it will learn and the more cases it will be able to identify correctly.
Method 2: Look at the problem differently
Sometimes adding more data couldn’t be the answer to your model inaccuracy problem. You’re providing your model with a good technique and the correct dataset. But you’re not getting the results you hope for; why?
Maybe you’re just asking the wrong questions or trying to hear the wrong story. Looking at the problem from a new perspective can add valuable information to your model and help you uncover hidden relationships between the story variables. Asking different questions may lead to better results and, eventually, better accuracy.Data Science Lingo 101: 10 Terms You Need to Know as a Data ScientistYour guide to understanding basic data science lingotowardsdatascience.com
Method 3: Add some context to your data.
Context is important in any situation, and training a machine learning model is no different. Sometimes, one point of data can’t tell a story, so you need to add more context for any algorithm we intend to apply to this data to have a good performance.
More context can always lead to a better understanding of the problem and, eventually, better performance of the model. Imagine I tell you I am selling a car, a BMW. That alone doesn’t give you much information about the car. But, if I add the color, model and distance traveled, then you’ll start to have a better picture of the car and its possible value.
Method 4: Finetune your hyperparameter
Training a machine learning model is a skill that you can only hone with practice. Yes, there are rules you can follow to train your model, but these rules don’t give you the answer your seeking, only the way to reach that answer.
However, to get the answer, you will need to do some trial and error until you reach your answer. When I first started learning the different machine learning algorithms, such as the K-means, I was lost on choosing the best number of clusters to reach the optimal results. The way to optimize the results is to tune its hyper-parameters. So, tuning the parameters of the algorithm will always lead to better accuracy.
Method 5: Train your model using cross-validation
In machine learning, cross-validation is a technique used to enhance the model training process by dividing the overall training set into smaller chunks and then use each chunk to train the model.
Using this approach, we can enhance the algorithm’s training process but train it using the different chunks and averaging over the result. Cross-validation is used to optimize the model’s performance. This approach is very popular because it’s so simple and easy to implement.7 Tips For Data Science NewbiesTo help make your learning journey easier.towardsdatascience.com
Method 6: Experiment with a different algorithm.
What if you tried all the approaches we talked about so far and your model still results in a low or average accuracy? What then?
Sometimes we choose an algorithm to implement that doesn’t really apply to the data we have, and so we don’t get the results we expect. Changing the algorithm, you’re using to implement your solution. Trying out different algorithms will lead you to uncover more details about your data and the story it’s trying to tell.
One of the most difficult things to learn as a new data scientist and to master as a professional one is improving your machine learning model’s accuracy. If you’re a freelance developer, own your own company, or have a role as a data scientist, having a high accuracy model can make or break your entire project.
A machine learning model with low accuracy can cause more than just financial loss. If the model is used in a sensitive scope, such as any medical application, an error in that model can lead to trauma and emotional loss for people involved with that model’s results.5 Reasons Why Every Data Scientist Should BlogExplaining something gives you a deeper understanding of it.towardsdatascience.com
Luckily, there are various simple yet efficient ways one can make to increase the accuracy of their model and save them much time, money, and effort that can be wasted on error mitigating if the model’s accuracy is low.
Improving the accuracy of a machine learning model is a skill that can only improve with practice. The more projects you build, the better your intuition will get about which approach you should use next time to improve your model’s accuracy. With time, your models will become more accurate and your projects more concrete.