Image Credit: dataconomy

When training a machine learning model, one of the most common question is, How many epochs should I run? The answer isn’t always straightforward. An epoch refers to one complete pass of the training dataset through the model. Running multiple epochs can bring big benefits, but also big risks if not handled with care.

Running more epochs often improves consistency. With repeated exposure to the data, models get better at generalizing patterns. It’s like practicing a skill repeatedly until it becomes second nature. Over time, the model fine tunes it’s internal weights to generate more accurate outputs, whether it’s classifying images or handling Selenium test scripts.

However, more isn’t always better. If you run too many epochs on a small dataset, the model might memorize the training examples instead of learning patterns. This is called overfitting, where the model performs well on training data but struggles with new, unseen inputs. It’s like tailoring a shirt to fit one person so perfectly that it won’t fit anyone else.

That’s why balance is key. Instead of focusing purely on increasing epochs, it’s often better to diversify the dataset. For example, 100 diverse examples trained with 1 epoch each can be more effective than 10 examples trained for 10 epochs. In short: quality and variety matter more than repetition.

Here’s a simple rule of thumb:

• For small datasets, use more epochs to extract more learning.
• For large datasets, fewer epochs may suffice to prevent overfitting.

Finding this balance is part of the art and science of machine learning and it makes all the difference in building reliable models.