Community Blog 6 Mistakes to Avoid When Building Machine Learning Models

6 Mistakes to Avoid When Building Machine Learning Models

This article highlights six common mistakes when building machine learning models.

In recent years, machine learning has gained more attention in academic research and practical application. It's not an easy task to build a machine learning model, for abundant knowledge, skills, and rich experience of developers are required to adapt the model to various scenarios. A valid, project need-satisfying machine learning model should center on data and base on business problems, with data and machine learning algorithms applied to solve problems.


When building a machine learning model, we need to avoid six common mistakes.

Mistake 1: Using Incorrectly Annotated Datasets

The first phase of any machine learning project is to develop an understanding of business requirements for a well-defined strategy to build a machine learning model. Another challenge for developers when training a model lies in getting correctly annotated data, which ensures the best outcome and makes the machine learning model more reliable for end users.

Mistake 2: Using Unverified Unstructured Data

Using unstructured data that has not been verified is one of the most common mistakes in AI development. It may cause problems during the operation of the machine learning model due to possible errors, such as duplicates, data hazards, and inadequate classifications. Therefore, developers must carefully examine the original dataset to delete unnecessary or irrelevant data before applying the data into machine learning training. By doing so, the accuracy of AI models can be ensured.

Mistake 3: Using Training Datasets with Insufficient Data

Insufficient data is a disadvantage in AI model development. Therefore, before model building, we need to prepare sufficient training data based on the AI model or industry features. For deep learning, more qualitative and quantitative datasets are needed to ensure high precision.

Mistake 4: Testing the Model with Data Already in Use

Machine learning models are built by learning and summarizing training data. By applying the acquired knowledge to new data, predictions are made to meet the demands. Therefore, we should avoid reusing the data that has been used for training. When testing the function of an AI model, it is important to use new datasets that have not been used for machine learning training.

Mistake 5: Only Focusing on the AI Model Learning

If we only train a machine learning model with training data, we won't see the differences between the real data and the training data and between test data and training data. The organization should pay attention to which approach it will take to validate and evaluate the performance of the model. Therefore, developers need to ensure that AI model learning is subject to the appropriate strategies, which requires regular checks on the AI training process and its results.

Mistake 6: Using a Biased AI Model

The data used in a machine learning model for training may bias the model due to various factors, such as age, gender, orientation, and income level, affecting the outcome. This effect needs to be minimized with statistical analysis to find out how each personal factor influences the processed data and AI training data.

When building a valid machine learning model, it is imperative to prepare well in the early stages, avoid mistakes, and improve constantly to meet the evolving business needs of the organization.

This article is retrieved and translated from 51CTO: https://ai.51cto.com/art/202104/660385.htm

Disclaimer: The views expressed herein are for reference only and don't necessarily represent the official views of Alibaba Cloud.

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