Key Points, Differences and Best Practices of Training and Testing in Machine Learning for Beginners

September 4, 2023September 4, 2023 Editor

Hi! In this article we will know in detail about “training” and “testing” in machine learning.

Both training and testing refer to two crucial phases in the development and evaluation of a machine learning model:

Training:

1. Training is the initial phase where a machine learning model learns patterns and relationships in the training data.

2. The training data entails some of input features (attributes or variables) and their corresponding target labels (the output or the value to be predicted).

3. During training, the model adjusts its internal parameters or weights using optimization algorithms (e.g., gradient descent) to minimize a predefined objective function (e.g., mean squared error for regression or cross-entropy for classification).

4. The training data process is involves three steps:

Feed – Feeding a model with data

Define – The model transforms training data into text vectors (numbers that represent data features). It can be done in Supervised learning model.

Test – Finally, you test your model by feeding it test data (unseen data).

5. The goal of training is to enable the model to make accurate predictions or classifications on new, unseen data.

Testing (or Evaluation):

1. After training, the model’s performance needs to be assessed to ensure it can generalize well to unseen data.

2. Testing involves using a separate dataset called the “test set” or “validation set” that was not used during the training phase.

3. The model makes predictions on this test set, and its performance is evaluated by comparing its predictions to the true target labels.

4. Common evaluation metrics include accuracy, precision, recall, F1-score for classification tasks, or mean squared error, R-squared for regression tasks, among others.

5. The test set’s performance metrics help assess how well the model generalizes to new, unseen data. This step is essential to ensure that the model does not overfit (memorize the training data) and can perform well in real-world applications.

6. It’s worth noting that the data used for testing should be entirely separate from the data used for training. We will know more on this later in this article.

7. Typically, the dataset is split into two or three parts: a training set, a validation set (used for hyperparameter tuning if necessary), and a test set. Cross-validation techniques can also be employed to make efficient use of data and get a more robust evaluation of the model’s performance.

The ultimate goal is to develop a machine learning model that performs well on unseen data, making it valuable for making predictions or decisions in real-world applications.

What are the personalities of quality training data?

Good, quality training data is a critical component of successful machine learning models. Here are attributes of valuable training data:

Relevance:

The data should be relevant to the problem you are trying to solve. Irrelevant or noisy data can confuse the model and hinder its ability to learn meaningful patterns.

Representativeness:

The training data should be a representative sample of the broader population or distribution that the model will encounter in the real world. It should cover all relevant scenarios and variations that the model is expected to handle.

Sufficiency:

There should be an adequate amount of data for the model to learn from. Insufficient data can lead to overfitting, where the model memorizes the training examples rather than generalizing from them.

Accuracy:

Data should be accurate and free from errors. Inaccurate data can mislead the model and result in incorrect predictions.

Completeness:

The dataset should be complete, meaning it contains all the necessary information required for the task. Missing values or incomplete records can be problematic for model training.

Consistency:

Data should be consistent in its format and structure. Inconsistent data can lead to difficulties in data preprocessing and model training.

Balanced Distribution:

For classification tasks, the classes should be relatively balanced in terms of the number of examples. Imbalanced datasets can bias the model toward the majority class and result in poor performance on minority classes.

Diversity:

The data should cover a diverse range of scenarios and edge cases. This diversity helps the model generalize well to new, unseen data.

Label Quality:

In supervised learning, if the data is labeled, the labels should be accurate and reliable. Incorrect labels can lead to a model learning incorrect patterns.

Temporal Relevance:

In some cases, data may have a temporal component. Ensure that the data reflects the temporal patterns and trends relevant to the problem.

Ethical Considerations:

Be aware of any ethical considerations related to the data, such as privacy and bias. Ensure that the data collection and usage align with ethical guidelines and regulations.

Data Exploration:

Conduct exploratory data analysis (EDA) to understand the characteristics of the data, identify outliers, and gain insights into potential feature engineering opportunities.

Data Cleaning:

Clean and preprocess the data to handle missing values, outliers, and other data quality issues. Data cleaning is often a necessary step in preparing the data for training.

Data Versioning:

Keep track of different versions of your training data to ensure reproducibility and traceability in model development.

Data Privacy and Security:

If the data contains sensitive or personal information, ensure that it is properly anonymized and secured to protect privacy and comply with data protection regulations.

Data Documentation:

Maintain thorough documentation that describes the data sources, collection methods, preprocessing steps, and any data transformations applied. This documentation is valuable for transparency and collaboration.

A reliable training data is not just about having a large dataset; it’s about having high-quality data that is relevant, representative, and free from errors, with proper documentation and consideration of ethical and privacy concerns. High-quality data forms the foundation for building robust and accurate machine learning models.

Finally, NEVER train on test data.

You that’s true! Training on test data is a fundamental mistake in machine learning and data science that should be avoided at all costs. The reason for this is that the test data is meant to be a completely independent and unseen dataset used exclusively for evaluating the model’s performance. Here’s why you should never train on test data:

Data Leakage:

When you train on test data, you introduce the risk of data leakage. Data leakage occurs when information from the test set inadvertently influences the model’s training process. This leads to overly optimistic performance estimates, as the model has essentially “seen” the test data during training.

Overfitting:

Training on test data can cause overfitting, where the model becomes overly specialized in predicting the specific examples in the test set. As a result, the model may perform well on the test data but generalize poorly to new, unseen data.

Invalid Evaluation:

If you train on test data, you no longer have an unbiased evaluation metric to assess your model’s true generalization performance. This defeats the purpose of having a separate test set, as you can no longer trust the performance metrics obtained from it.

To avoid training on test data, following are the best practices:

Data Splitting: Split your dataset into three distinct subsets: a training set, a validation set, and a test set. The training set is used for model training, the validation set for hyperparameter tuning, and the test set for final model evaluation.

Holdout Strategy: Keep the test set completely separate from the training and validation sets. The test set should only be used after model training is complete, and you are ready to assess its real-world performance.

Cross-Validation: In cases where data is limited, consider using cross-validation techniques (e.g., k-fold cross-validation) to make efficient use of your data while ensuring that test data remains untouched during training.

Strict Data Management: Maintain strict separation between training, validation, and test data throughout the machine learning pipeline. Be mindful not to inadvertently mix these datasets.

Documentation: Document your data splitting and model evaluation process to ensure transparency and reproducibility in your machine learning workflow.

In abstract, adhering to the principle of never training on test data is crucial for obtaining accurate and reliable performance estimates for your machine learning models and ensuring their ability to generalize to new, unseen data.