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Community Blog 6 Mistakes To Avoid While Building Your Machine Learning Model

6 Mistakes To Avoid While Building Your Machine Learning Model

When building a machine learning model, we should avoid the 6 mistakes.

In recent years, machine learning has received more and more attention in the field of academic research and practical applications. But building a machine learning model is not a simple matter. It requires a lot of knowledge and skills and rich experience to make the model work in a variety of scenarios. The correct machine learning model should be data-centric and based on an understanding of business problems. And data and machine learning algorithms must be applied to solve problems in order to build a machine learning model that can meet the needs of the project.

When building a machine learning model, we should avoid the following 6 mistakes:

1. Not using properly labeled data sets

The first stage of any machine learning project is to develop an understanding of business needs. When building a machine learning model, you need a clearly defined strategy. When training a model, obtaining the correct labeled data is another challenge facing developers. This not only helps you get the best results but also makes machine learning models appear more reliable among end-users.

2. Use unverified unstructured data

Using unverified unstructured data may cause problems in the operation of the machine learning model. Because unverified data may have errors, such as duplication, data conflicts, lack of classification, etc. Using unverified unstructured data is one of the most common mistakes made by machine learning engineers in AI development. Therefore, before using the data for machine learning training, you need to carefully check the original data set and eliminate unnecessary or irrelevant data to help the AI model perform its functions with higher accuracy.

3. Insufficient training data set

If the data is insufficient, it will reduce the probability of success of the AI model. Therefore, before starting to build a machine learning model, we need to prepare sufficient training data according to the type of AI model or industry.
If it is deep learning, more qualitative and quantitative data sets are needed to ensure that the model can run with high precision.

4. Use data already in use to test the model

The machine learning model is constructed by learning and generalizing training data, and then applying the acquired knowledge to new data that has never been seen before to make predictions and achieve its goals. Therefore, we should avoid reusing the data that has been used to test the model. When testing the function of the AI model, it is very important to use a new data set that has not been used for machine learning training before.

5. Relying solely on AI model learning

When training a machine learning model, if we repeat it all the time, we will not know whether there are any differences between real-world data and training data, as well as test data and training data, and what methods the organization will take to verify and evaluate the performance of the model. Therefore, developers need to ensure that the AI model learns with the correct strategy. To ensure this, you must regularly check the AI training process and its results to get the best results.

6. Make sure your AI model is unbiased

The data used in training the machine learning model may make the model biased due to various factors such as age, gender, orientation, and income level, which can affect the results in some way. Therefore, you need to find out how each individual factor affects the processed data and AI training data by using statistical analysis to minimize this phenomenon.

Conclusion

To succeed in the construction of machine learning models, the most important thing is to be prepared in the early stage, avoid mistakes, and constantly look for improvements and better ways to meet the evolving business needs of the organization.

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Over the past few decades, there have been many types of machine learning methods published in research and publications, and there can be multiple classification methods based on the emphasis on different aspects.

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In order to better understand artificial intelligence(AI), this article explains the meaning of these words in the simplest language and clarifies the relationship between them, hoping to be helpful to people who are just getting started.

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