Model accuracy is very important for companies as errors could cause huge impacts on their system.
Finally, you have built a machine learning model. Congratulations! But before making it mainstream, you need to make sure it performs well. In order to do that, you must check the model accuracy. There are many ways to measure the performance of the model. One significant way is to predict the error percentage and another widely used way is receiver operating characteristic (ROC) curve.
Evaluating your machine learning algorithm is a mandatory step that you need to perform before streamlining the model. However, accuracy is the prime metric to compare models, but models are not based on accuracy only. Some of them are classification metrics, precision, recall, regression metrics, explained variance, mean squared error, etc. Fortunately, model accuracy stands out of all other metrics as it defines how robust the model is, how it will perform on a different dataset and how much flexibility it has to offer. A machine learning model bags the highest accuracy when it has realized and learned about the data correctly and desirably. Henceforth, the predictions made by it are close to the actual values. On the other hand, receiver operating characteristic (ROC) curve is created by plotting the true positive rate of the classifier against the false positive rate at various threshold settings. The machine learning performance is also measured on the confusion matrix. In binary classification problems, the confusion matrix is a 2×2 matrix containing the true positive, false positive, true negative, and false-negative rates. In this article, we’ll take you through model accuracy and what can be done to improve it.
Model accuracy is defined as the number of classifications a model correctly predicts divided by the total number of predictions made. Businesses use machine learning models to make practical decisions, and more accurate model outcomes result in better decisions. Model accuracy is very important for companies as an error could cause huge impacts on their working system. Even though developing a more accurate model doesn’t directly result in a corresponding profit increase, it is often beneficial across the board. For example, a patient wrongly diagnosed with disease cause problem to both the patient and the hospital. Therefore, companies and tech geeks put their efforts into model accuracy to avoid the considerable time, money, and undue stress.
Ways to improve model accuracy
A lot of developers find themselves stuck at the dead-end of improving the model accuracy. Even though it is tough, they have to redo the model to save the investment that the company has put on it. We have listed some ways that could help you enhance the model performance.
Adding more data: Having more data to feed the machine learning model is like a gift for developers. It helps them to rely solely on data rather than assumptions and weak correlations. More data results in better and accurate models.
Feature engineering: Feature engineering helps to extract more information from existing data. New information is extracted in terms of new features and it has the higher ability to explain the variance in the training data.
Multiple algorithms: Even though hitting at the machine learning algorithm is the best way to improve accuracy, it is easier said than done. While some algorithms are better suited for a particular type of data, some others won’t go well. Henceforth, the developers should apply all relevant models and check the performance.
The risk of improving model accuracy
When the developers come to know that the machine learning model they designed has very little accuracy, they try to improve it through various methods. One major move to increase the accuracy is by tuning model parameters, which is also called parameter search. This can be achieved by making relatively large changes on the parameter one is tuning, and then successively smaller changes as one settles closer to an optimum value.
For image classification accuracy, convolutional neural networks are used to make the machine learning model work better. If one had started on an image classification problem with a simple logistic regression over pixel values in the image and wasn’t getting the hoped for performance, one should switch to a completely different model like the convolutional neural network for a more accurate reading.
The risk in both the model accuracy improvement techniques is that the machine learning model is trained with the entire dataset. The developer engages in adding all the previous models and faces the danger of creating an entirely different model to the one he/she aspired for. Things get even worse at times as the machine learning model needs substantial improvement. It could risk the whole investment on the model in that case. Fortunately, the emerging new model might provide a substantial improvement which is worth taking the risk.