Understanding the Impact of Data Shuffling in Keras: Why train_test_split() Outperforms model.fit()

Описание: Discover the reasons why using `train_test_split()` for data shuffling makes a significant difference in model accuracy compared to `model.fit()` in Keras. Unlock better performance in your machine learning projects!
---
This video is based on the question https://stackoverflow.com/q/65928428/ asked by the user 'Tayfe' ( https://stackoverflow.com/u/6514703/ ) and on the answer https://stackoverflow.com/a/65929059/ provided by the user 'Gerry P' ( https://stackoverflow.com/u/10798917/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

Visit these links for original content and any more details, such as alternate solutions, latest updates/developments on topic, comments, revision history etc. For example, the original title of the Question was: Keras: Shuffling data using model.fit() doesn't make a change but sklearn.train_test_split() does

Also, Content (except music) licensed under CC BY-SA https://meta.stackexchange.com/help/l...
The original Question post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license, and the original Answer post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license.

If anything seems off to you, please feel free to write me at vlogize [AT] gmail [DOT] com.
---
Understanding the Impact of Data Shuffling in Keras

In the world of machine learning, data preparation is often as crucial as the model itself. A common question that arises, especially among newcomers to Keras, is: Why does using train_test_split() to shuffle data produce better validation accuracy than using the shuffle parameter in model.fit()? This guide will dive deep into this issue and clarify the important nuances of shuffling your data in Keras.

The Mystery of Low Validation Accuracy

Let's set the stage. You have a model built using Keras, and after training on a dataset, you're observing a validation accuracy (val_accuracy) of about 50%. However, when you shuffle your data with train_test_split() before training, your model's accuracy skyrockets to over 80%.

This leads to questions:

Does shuffling the data before training genuinely affect results?

What is the purpose of the shuffle argument in model.fit() if it's ineffective?

Are there hidden mechanisms in train_test_split() that make it more beneficial for our training process?

Shuffling Data: The Role of train_test_split()

Understanding train_test_split()

train_test_split() is a function from sklearn that randomly splits your dataset into training and testing sets. Here are some key features:

It randomly shuffles the dataset before splitting.

Each time you run it, you can get a different split by setting the random_state.

The Impact of the Train-Test Split

When you uncomment the line calling train_test_split() in your code, the model gets a totally different training set each time, depending on the randomness introduced by the shuffle. This means you are effectively taking a sample of data that may contain a more balanced distribution of classes, which gives your model a better chance to learn robust features.

The Mechanics of Keras model.fit()

Validation Split Mechanism

Here's the crux of the issue with the shuffle parameter in model.fit():

The documentation specifies that when you use a validation split, Keras takes a segment from the end of your input data.

If your data is not shuffled beforehand, this can lead to biased validation samples, especially if there is a pattern (like class distribution) toward the end of the dataset.

Why This Cases Trouble

If you train your model without prior randomization and utilize the last portion of data as validation, there's a significant risk that your validation set may not accurately represent the diversity of the training data. Particularly, if the last samples contain similar characteristics (like images of only cats in a cat vs. dog scenario), the model's validation accuracy will appear lower.

Solutions and Best Practices

To ensure that your machine learning model performs optimally, consider these best practices:

Shuffle your training data before fitting: Always use a mechanism, like train_test_split(), to ensure your training data is randomized.

Ensure diversity in validation sets: If using model.fit() with a validation split, always start with a shuffled dataset to avoid biased validation results.

Understand your dataset: Always visualize and analyze your dataset to check for any patterns that might mislead your model evaluation.

Conclusion

Data shuffling is a key step in the machine learning pipeline, especially when it comes to model fitting and validation. Understanding the distinct functionalities of functions like train_test_split() and model.fit() can dramatically influence your model's performance and accuracy. By incorporating randomization into your data preparation, you pave the way for more generalized and robust models th