Top Model Selection Techniques in Machine Learning Projects

Brain John Aboze

| August 01, 2023 | 10 mins |

Introduction

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In machine learning (ML), no single universal model can cater to every dataset or business problem. Every model has its capabilities, strengths, and weaknesses. Their applicability varies based on the dataset format, quality, and the problem they are trying to solve. This brings us to the concept of ML model selection, a critical step in the ML development lifecycle.

Model selection is a machine learning process used to choose the best model for a given task from a collection of candidate models. The candidate models are assessed using different model selection techniques. The outcome of model selection relies on a robust validation strategy and appropriate evaluation metrics (discussed below) that can quantitatively verify the quality of the model.

Let’s explore ML model selection in detail and list some prominent model selection and validation techniques.

Why is Model Selection in Machine Learning Important?

ML models often work well in controlled academic settings but fail in the production environment, especially on an industrial scale. That’s because the real-world environment has a number of factors (discussed in the next section) to consider that can limit the performance of the ML model. Hence, rigorous model selection is needed, which can significantly impact the model’s performance and accuracy for real business problems.

Besides finding the best-suited model for a particular task, model selection is important for several other reasons, such as:

• Ensures that the model’s predictive performance is generalizable to unseen real-world data.
• Helps avoid overfitting or underfitting, which can occur if the model is poorly trained. As a result, ML model selection optimizes the model with an appropriate bias-variance trade-off.
• Helps balance the model’s performance and the cost of computational resources used to build the model.

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5 Important Factors to Consider While Selecting an Appropriate Machine Learning Model?

While the best-performing ML model is required for any task, performance is not necessarily the only factor to consider when selecting a model. Some other prominent factors include:

1. Dataset Size & Format

Different ML models are designed to handle specific data types. For instance, artificial neural networks excel in processing vast amounts of numerical data, while transformer models work well for Natural Language Processing (NLP) tasks.

2. Training Time, Inference Time & Associated Costs

ML models, especially enterprise-grade, can take days or months to train. For instance, researchers estimate that the time required to train a GPT-3 model with 175 billion parameters is 34 days.

3. Performance Metrics

Evaluating machine learning models is a critical step in the ML development lifecycle. Different ML models require different evaluation metrics to monitor and evaluate their performance. For instance, some prominent ML tasks and their suitable evaluation metrics are given below.

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4. Explainability

ML algorithms operate like black boxes, i.e., data goes in – the model processes it and generates an outcome. It’s difficult to explain how they reach an outcome.

5. Complexity

Complex ML models can capture more details from large datasets but are difficult to maintain.

Top Model Selection Techniques in Machine Learning Projects

• Version Comparison
• AI-Assisted Annotations
• CI/CD for LLMs
• LLM Monitoring

Top ML Model Selection & Validation Techniques

Based on the factors discussed above, how to choose an optimal ML model? To answer this question, you must first understand which model selection techniques are available.

ML model selection techniques are categorized into two groups: probabilistic and resampling methods.

3 Prominent Probabilistic Techniques for Model Selection

1. Akaike Information Criterion (AIC)

The Akaike Information Criterion (AIC) measures the quality of a statistical model for a given dataset. It balances the trade-off between the goodness of the model’s fit and the complexity of the model. AIC penalizes models with more parameters, encouraging the selection of simpler models that still represent the training data well. A lower AIC value indicates a better-fitting model.

Formula:

AIC = 2k – 2ln(L)

2. Bayesian Information Criterion (BIC)

Derived from Bayesian probability and inference, the Bayesian Information Criterion (BIC) is a model selection statistic similar to AIC but includes a stronger penalty for model complexity. BIC is particularly suitable for models trained using maximum likelihood estimation.

Formula:

BIC = -2ln(L) + kln(N)

3. Minimum Description Length (MDL)

The Minimum Description Length (MDL) method aims to find the model that best balances the complexity and goodness of the model’s fit by minimizing the total description length (in bits) of the model and the data it explains.

Formula:

MDL = L(h) + L(D | h)

3 Prominent Resampling Techniques For Model Selection

Some commonly used resampling techniques are:

1. Random or Time-Based Split

To generate new data samples, the training dataset can be split into multiple sets. The split can be random or time-based.

2. Bootstrap

The bootstrap model selection technique creates a stabilized model by resampling data points from the original dataset with replacement.

3. Cross-Validation

Cross-validation is one of the most commonly used resampling techniques for ML model selection.

Example Code

import numpy as np
from sklearn.model_selection import train_test_split

X_data = range(10)
y_data = range(10)
for i in range(5):
    X_train, X_test, y_train, y_test = train_test_split(X_data, y_data, test_size = 0.3, random_state = None)
    print(y_test)

Output:
[4, 2, 8]
[1, 6, 0]
[3, 8, 9]
[1, 3, 5]
[5, 3, 2]

Streamline Your ML Model Selection Process To Maximize Performance

ML model selection presents significant challenges. As presented in the article, there are a multitude of methods available for model selection. How can you choose the best one?
You can experiment with multiple techniques and try to interpret their results.