Common Challenges and Pitfalls in Machine Learning

While machine learning (ML) projects can yield great rewards, there are several common challenges and pitfalls that teams run across. Understanding these difficulties will enable you to deal with them successfully

1. Data Quantity and Quality: The effectiveness of machine learning models can be greatly impacted by inadequate or low-quality data. Preprocessing, cleaning, and ensuring there is enough data are essential.

2. Lack of Domain Knowledge: ML models frequently call for thoroughly comprehending the issue domain. Insufficient topic expertise may result in incorrect interpretation of findings and model malfunctions.

3. Data Leakage: Mixing training and testing data or using future information can lead to data leakage, making your model appear more accurate than it is.

4. Imbalanced Data: In classification problems, imbalanced class distributions can cause models to be biased toward the majority class and perform poorly on the minority class.

5. Interpretability: In fields where regulations apply or where interpretability is essential, complex models like deep neural networks may be challenging to understand.

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