AI models are increasingly driving important decisions across businesses. In the finance sector, they’re evaluating credit risk and loan applications; in manufacturing, they’re tasked with quality control; and in medicine, they’re contributing to better diagnoses and treatment plans. What makes AI models so effective at their tasks is training. Simply put,  training AI is the process of teaching an AI model how to make predictions or generate a certain output using data. 

The model training process 

Before getting to avoidable mistakes, it’s crucial to understand the AI model training process and how it works. Training usually includes five steps to help ensure the model produces accurate and consistent results.  

Step 1: Data preparation 

Creating a reliable AI model begins with good data. Datasets should reflect real-life instances and be free of bias and errors. 

Step 2: Model selection 

Choose a model that fits your goals. Your choice depends on your project parameters, resources, compute requirements, costs, complexity and many other factors. Common models include linear regression, decision trees, random forests, and logistic regression among others.  

Step 3: Commence the training 

Start your model off with the basics. The goal is to achieve results within expected parameters and have your model learn and improve.  

Step 4: Validate training results 

After the initial training, your model should be able to produce reliable results. Teams challenge and validate their model’s abilities using a different dataset and evaluating model output. 

Step 5: Testing 

The final step is to use real-world data to test the model’s performance and accuracy. If the model produces the desired results, the training has been largely successful. If not, more training may be needed. 

Training mistakes to steer clear of 

Training is an iterative process, it usually takes many adjustments to get the results you want. However, training errors may prolong training time and delay deployment. We’re rounded up some common training mistakes and offered tips on how to fix them. 

Bad quality data 

An efficient and high-performing model has to be trained on vast quantities of good quality data. Inconsistent or biased data affects the entire training process and ultimately leads to inaccurate results. Common dataset issues include: 

• Labeling errors 

• Irrelevant data 

• Poorly formatted data 

• Undesirable content (such as offensive or explicit material) 

Data solutions: 

• Use datasets from reputable sources such as government agencies or research institutes.  

• Implement robust data processing measures. Remove duplicates or outliers that could warp model output.  

• Make sure your dataset is diverse and free of biases. 

Overfitting or underfitting the model 

Overfitting is when a model perfectly memorizes training data but can’t yield results on new data. The model has trouble generalizing the concepts and applying them to new data. Overfitting can happen when you don’t have enough training data for the model. 

Underfitting refers to the opposite problem. The model can’t establish patterns within the data and may make incorrect predictions. Underfitting can be the result of insufficient training time or a model that’s too simple for the dataset. 

Overfitting solutions: 

• Correct overfitting through regularization methods like L1 and L2 

• Increase the amount of training data 

• Simplify your model or consider early stopping to prevent overtraining 

Underfitting solutions: 

• Fix underfitting by adding more layers or features to your model to make it more complex 

• Increase model training time 

• Remove noise or irrelevant details from your dataset to simplify patterns 

Data leakage 

Data leakage is when a model uses information from training that would not be available for real-world predictions. Data leakage makes the model results look perfectly accurate until it’s finally deployed. Once deployed, the model produces incorrect results. Data leakage may be caused by: 

• Including information in training data that would not be shared in real-life applications 

• Data contamination (combining test data sets with training data) 

• Incorrect cross-validation of data 

• Data preprocessing mistakes (such as scaling the data before separating it into sets for training and validation) 

Data leakage prevention tips: 

• Preprocess data for training and test sets separately 

• Split data into training and test sets carefully (for instance, split time-dependent data chronologically to prevent data contamination 

• Consider k-fold cross-validation for a more robust test of model performance 

Incorrect hyperparameter tuning 

Hyperparameters are configured before model training begins. Hyperparameters aren’t learned from data, instead they’re chosen by the developer. They influence how a model learns, its complexity, and its ability to generalize data. Using default values or making hyperparameter adjustments at random can negatively impact model performance. However, the right settings can minimize loss function or improve accuracy, precision, and recall.     

Hyperparameter tuning solutions: 

• Try techniques like grid search, random search, and Bayesian optimization to help identify the most suitable configurations 

• Consider using automated machine learning (AutoML) tools to help with hyperparameter tuning, where possible 

Neglecting feature engineering 

Feature engineering involves turning raw data into an actionable format that can improve the performance of a model. Badly selected features prevent your model from generating accurate results and increase the odds of overfitting. Relying on auto-feature selection may make it harder to understand how the model makes predictions.   

Feature engineering solutions: 

• Use techniques like Principal Component Analysis (PCA) to reduce the number of predictive variables needed for accurate generalization 

• Employ standardization and normalization techniques to help your model make sense of numerical data 

• Try recursive feature elimination to make sure your model isn’t caught up in irrelevant details 

Training is one of the most crucial aspects of building a successful machine learning model. But getting it right requires a good understanding of data processing and model tuning. Avoiding common training mistakes can help you build models that are more accurate and reliable. 

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