ML Fundamentals Day 17/30: What Is a Loss Function? (How a Model Knows It’s Wrong)

2026-07-25 · Source: Deep Learning on Medium · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Novice, short

Summary

Loss functions are mathematical formulas that quantify the discrepancy between a machine learning model's predicted output and the actual true output, essentially measuring "how wrong" a prediction is. During training, a model's primary goal is to minimize this loss value, indicating improved prediction accuracy. The process involves the model making a prediction, comparing it to the actual value using the loss function, and then employing an optimization algorithm like gradient descent to adjust internal parameters (weights and biases) to reduce future errors. Common types include Mean Squared Error (MSE) and Mean Absolute Error (MAE) for regression problems, and Binary Cross-Entropy and Categorical Cross-Entropy for binary and multi-class classification, respectively. These functions are crucial for guiding model learning, evaluating performance, and informing algorithm selection based on problem type.

Key takeaway

For AI students building and training machine learning models, understanding loss functions is critical for effective model development. You should carefully select the appropriate loss function, such as MSE for regression with outlier sensitivity or Binary Cross-Entropy for binary classification, as this choice directly impacts your model's learning objective and overall performance. Incorrect selection can lead to suboptimal training and inaccurate predictions.

Key insights

Loss functions quantify model prediction error, providing essential feedback for optimization algorithms to adjust parameters and improve accuracy.

Principles

• Minimize loss for better model performance.
• Problem type dictates loss function choice.
• Loss functions guide model parameter optimization.

Method

A model predicts, compares its output to the actual value via a loss function, calculates error, then uses an optimization algorithm (e.g., gradient descent) to adjust parameters, reducing future errors.

In practice

• Apply MSE for regression, penalizing large errors.
• Use MAE for regression, robust to outliers.
• Select Binary Cross-Entropy for binary classification.

Topics

• Loss Functions
• Machine Learning Training
• Regression Models
• Classification Models
• Mean Squared Error
• Cross-Entropy

Best for: AI Student

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Editorial summary, takeaway, and curation by AIssential. Original article published by Deep Learning on Medium.