Creating Training and Evaluation Sets in Machine Learning

In the journey of developing a machine learning model, one of the foundational steps is partitioning our dataset into training and evaluation sets. But why is this step so crucial?

The Importance of Creating Training and Evaluation Sets in Machine Learning

Ensuring model validity and effectiveness
The primary goal of any machine learning model is to make accurate predictions on new, unseen data. To ensure our model’s validity, we divide our data into training and evaluation sets. This division ensures that our model’s effectiveness isn’t just because it has memorized the training data but has genuinely recognized underlying patterns. The evaluation set acts as a stand-in for real-world data. By testing our model against this set, we simulate its performance in real-world scenarios, ensuring it’s not just regurgitating what it has seen but making informed predictions.

Preventing overfitting
A common pitfall in machine learning is overfitting, where a model performs exceptionally well on the training data but poorly on new data. Here’s how partitioning helps. If we were to use our entire dataset for training, we might miss signs of overfitting. By setting aside an evaluation set, we can detect if our model is overly adapted to the training data.
An evaluation set helps ensure that our model’s performance remains consistent and doesn’t degrade when exposed to new data.

Tuning model parameters
Machine learning models come with various parameters that can be adjusted to optimize performance. The evaluation set plays a pivotal role here. By testing our model on the evaluation set, we can observe its performance and make necessary adjustments to its parameters. This iterative process of training, evaluating, and tuning helps in refining our model to its best version.

Providing unbiased evaluation
An unbiased evaluation is crucial for understanding the true potential of a machine learning model. By using distinct training and evaluation sets, we get a clearer, unbiased picture of how our model might perform on data it hasn’t seen before. This separation can highlight any biases in our model’s predictions, allowing us to address them before the model is put to real-world use.

Ensuring real-world applicability
Ultimately, the goal of any machine learning model is to be useful in real-world applications. By using an evaluation set, we simulate the model’s performance in real-world contexts. If our model hasn’t been exposed to the evaluation data during its training phase, it’s more likely to make accurate and unbiased predictions when faced with real-world data.


Creating Training and Evaluation Sets for Machine Learning

Understand the need to split the data
Before diving into the mechanics of data splitting, it’s crucial to understand its significance. When we talk about machine learning, we often emphasize the importance of training a model. But how do we know if our model is genuinely learning and not just memorizing? Imagine you’re studying for an exam. If you only study the questions and their exact answers without understanding the underlying concepts, you might struggle if the exam has different questions. Similarly, in machine learning, we want our models to understand underlying patterns and not just memorize the data. This is where the concept of splitting our dataset comes into play. By dividing our dataset into distinct sets – training, validation, and test sets, we ensure that our model learns from a portion of the data and then gets evaluated on unseen data.

  • Overfitting is akin to a student who memorizes exam questions but struggles with different questions on the same topic. A model that overfits has learned the training data too well, including its noise and outliers, and performs poorly on new, unseen data. By splitting our data, we can train our model on one subset and test its performance on another, ensuring it generalizes well to new data.

Choosing the right split ratio
While there’s no one-size-fits-all, a typical split ratio is 70% for training, 15% for validation, and 15% for testing. However, depending on the size and nature of your dataset, these percentages can vary. It’s essential to ensure that each set has enough data to be representative. In datasets where certain classes are underrepresented, random splitting might lead to sets without any representation of these classes.

  • Stratified sampling ensures that each set has a proportional representation of each class, making it especially valuable for imbalanced datasets.

Splitting of the dataset
One of the most common tools for data splitting in Python is Scikit-learn’s train_test_split function. This function allows for easy and customizable data splitting.

  • Cross-validation involves creating multiple training and test splits and averaging the results, providing a more robust measure of model performance. This technique is especially useful when working with smaller datasets.

Evaluate the splitting
After splitting, it’s essential to ensure that the training and test sets are similar regarding key variable distribution. This ensures that our model is not biased and can generalize well.

  • Tools like summary statistics and visual plots, such as histograms, can help in comparing the distribution of data in the training and test sets, ensuring they are similar.

Adjusting the split based on model performance
Always keep an eye on your model’s performance. If it performs significantly better on the training data than the test data, it might be overfitting. If overfitting occurs, consider adjusting the training/validation/test split.

  • Techniques like regularization or even gathering more data can also help.

Importance of reproducibility
When splitting data, always set a seed or random state. This ensures that if you or someone else reruns your analysis, the results remain consistent.

  • K-fold cross-validation involves splitting the dataset into ‘k’ subsets. The model is trained on k-1 of these subsets and tested on the remaining one. This process is repeated k  times, ensuring more reproducible results.