How to Choose the Best Machine Learning Model in Data Science: Balancing Theory and Experimentation

8 min readJust now

As a data analyst or data scientist, one of the major tasks is to decide on the best model or technique for any given project. This often involves a combination of domain knowledge, experience, and trial and error to find the best fit for the data at hand. This is a standard and widely accepted practice in the field.

Why Trial-and-Error is Common in Data Science

Data is Often Complex and Unique:

Every dataset has its peculiarities, such as outliers, missing values, noise, and specific distributions. Because of this, there is rarely a one-size-fits-all model. Different models and techniques can behave differently depending on the dataset’s characteristics.
A model that performs well in one context might underperform in another. Therefore, it is common practice to try multiple models and techniques to find the best fit.

2. No Perfect Model Exists:

Each model has strengths and weaknesses. For example:

Linear Regression works well for linear relationships but struggles with non-linearity.
Decision Trees are great for interpretability but prone to overfitting.
Neural Networks handle complex patterns but require large amounts of data and are hard to interpret.

This diversity of strengths requires testing and validation to ensure the right choice for a specific use case.

3. Performance Metrics and Validation:

Data scientists rely on metrics like accuracy, precision, recall, F1-score, ROC-AUC, and others to validate model performance. Only after trying and comparing different models using these metrics can a decision be made about the best fit.
Cross-validation techniques, such as K-fold cross-validation, are used to validate a model's stability and robustness across different data splits, further highlighting the importance of trial and error.

Some Sources That Support the Trial-and-Error Approach

Practical Data Science:

According to “Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow” by Aurélien Géron, the author emphasizes the importance of testing different models:
“You cannot know which model will perform best without testing because each algorithm makes different assumptions about the data. Testing multiple algorithms, however, is a necessary part of machine learning.”

2. Data Science Competitions:

Platforms like Kaggle and DrivenData highlight that the top performers often try multiple models, hyperparameters, and feature engineering techniques. They iterate through many versions of their models before settling on the best-performing one.
In the “Data Science for Business” by Foster Provost and Tom Fawcett book, the authors discuss the iterative nature of data science:
“Building predictive models often involves experimenting with different algorithms and parameters. This exploration is essential to identify what works best for a particular problem.”

Some Machine Learning Algorithms and Data Science Best Practices:

In “Machine Learning Yearning” by Andrew Ng, he highlights that even with a clear understanding of the problem, testing and trying different models is often needed to achieve optimal results.
An article by Harvard Business Review emphasizes that selecting the right algorithm and hyperparameters often requires testing different methods, validating them with appropriate metrics, and refining them based on outcomes—source: Harvard Business Review — The Importance of Data Science in Decision Making.

Domain Expertise Matters:

As much as trial and error is necessary, it’s not random guessing. A data scientist’s domain knowledge plays a critical role in making informed decisions about:
Feature Engineering: Selecting or transforming features based on the nature of the data.
Algorithm Choice: Choosing a starting model based on the data’s structure (e.g., using Logistic Regression for a binary classification problem).
Hyperparameter Tuning: Adjusting model parameters like learning rate, regularization strength, etc., in a guided way.

Best Practices to Guide the Process:

Start with Simple Models:

To understand the data and set a performance baseline, begin with simple, interpretable models like linear regression or decision trees.
As the need arises, progress to more complex models like Random Forests, Gradient Boosting, or Neural Networks.

Are Random Forests, Gradient Boosting, or Neural Networks really more complex than linear regression and decision trees?

Yes, Random Forests, Gradient Boosting, and Neural Networks are more complex models than Linear Regression and Decision Trees. Here’s a breakdown of why that is, along with what makes them more sophisticated:

Complexity in Terms of Model Structure:

Linear Regression:

It’s one of the simplest models. It fits a linear equation (straight line) to the data. The idea is to find the best-fit line by minimizing the difference between actual and predicted values.
Complexity is low because it’s just finding the relationship between features and a target using weights/coefficients.

Decision Trees:

A Decision Tree splits the data into branches based on feature values. Each split is a decision based on a single feature, making the model easy to interpret.
While Decision Trees can get deep (having many branches), they are still simple because each decision point is based on a clear criterion (like “Is this feature > X?”).
Complexity is relatively low because it’s based on simple binary splits.

Random Forests:

An ensemble of multiple Decision Trees is what makes up A\a Random Forest. Each tree is built on a different random sample of data and/or features, and predictions are made by aggregating the results of all the trees (e.g., majority voting or averaging).
The complexity increases because you have to manage many trees, average their results, and tune hyperparameters like the number of trees or depth.

Gradient Boosting:

Gradient boosting is another ensemble technique where models (often Decision Trees) are built sequentially. Each new model corrects the errors made by the previous ones, leading to a final model that combines the strengths of each step.
Complexity is higher because it involves iterative training and managing residuals (errors), and the models are often sensitive to hyperparameters like the learning rate, number of estimators, and tree depth.

Neural Networks:

Neural Networks, inspired by the human brain, involve layers of interconnected nodes (neurons). Each node in a layer applies mathematical operations (like weights, biases, and activation functions) to produce an output.
The complexity is very high due to multiple hidden layers, numerous parameters (weights and biases), non-linear activation functions, and the need for extensive data preprocessing.
Careful tuning of parameters are require here, like the number of layers, learning rate, and the architecture of the network, making them more complex to train and interpret.

Complexity in Terms of Interpretability:

Linear Regression: Very interpretable because the coefficients show how each feature impacts the target variable.
Decision Trees: Easy to interpret as you can visualize the tree structure and understand each decision path.
Random Forests: Harder to interpret because it’s a combination of many trees. While you can get feature importance scores, understanding the decision-making of the entire forest is complex.
Gradient Boosting: Even harder to interpret because it builds trees sequentially and is highly sensitive to small changes in data.
Neural Networks: Often called a “black box” model because the weights and activations make it hard to understand the decision process, even if you have a well-performing model.

2. Complexity in Terms of Computational Requirements:

Linear Regression: Fast to train and requires minimal computational resources because it’s just solving a set of equations.
Decision Trees: Computationally light, though deep trees can become computationally intensive if not pruned.
Random Forests: Computationally more demanding because you’re training many trees. Each tree must be trained and predictions are based on aggregating results from all trees.
Gradient Boosting: More computationally intensive than Random Forests because each tree corrects errors from the previous one, leading to a potentially larger number of trees and more complex model management.
Neural Networks: Very computationally demanding, especially deep networks. Neural Networks require substantial processing power, large amounts of data, and potentially GPUs/TPUs for effective training.

3. Complexity in Terms of Tuning and Hyperparameters:

Linear Regression: Minimal tuning required (usually just regularization parameters like Lasso or Ridge).
Decision Trees: Tuning involves choosing max depth, min samples per leaf, and pruning strategies.
Random Forests: Hyperparameters like the number of trees, max depth, and the number of features to consider at each split can significantly impact performance.
Gradient Boosting: Requires tuning multiple parameters like learning rate, number of estimators, max depth, and subsampling rate. It’s more sensitive to these parameters than Random Forests.
Neural Networks: Highly sensitive to a large number of hyperparameters, including learning rate, the number of layers, the number of neurons per layer, activation functions, dropout rates, and optimizers.

Real-World Use Cases:

Linear Regression: Predicting housing prices based on features like size, location, and number of bedrooms. It’s used when the relationship is linear.
Decision Trees: Customer segmentation for a marketing campaign, where decisions are based on factors like age, income, and shopping habits.
Random Forests: Predicting credit card fraud, where a wide variety of factors need to be considered, and the robustness of the model is crucial.
Gradient Boosting: Used in Kaggle competitions or real-world predictive tasks like forecasting sales or predicting customer churn, where accuracy is paramount.
Neural Networks: Image classification, natural language processing, or any task requiring recognition of complex patterns (e.g., facial recognition, sentiment analysis).

In summary:

Linear Regression and Decision Trees are simpler models that are easy to understand, quick to train, and computationally light. They are suitable for smaller datasets and problems where interpretability is key.
Random Forests, Gradient Boosting, and Neural Networks are more complex because they involve ensembles, sequential corrections, or intricate network structures. These require more data, computational resources, and careful tuning, but they also handle non-linear relationships and high-dimensional data better, making them powerful for complex tasks.

References Supporting the Complexity:

“Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow” by Aurélien Géron emphasizes the complexity differences between simple models (like Linear Regression) and complex models (like Neural Networks).
“The Elements of Statistical Learning” by Hastie, Tibshirani, and Friedman discusses why certain models are considered more sophisticated based on their structure and computational needs.
An article on Towards Data Science, “Understanding the Complexity of Machine Learning Models” highlights why ensemble methods like Random Forests and Neural Networks require more data, tuning, and computational power compared to simpler models. Link to article

The increased complexity of more advanced models is justified when the problem requires capturing complex patterns or non-linear relationships in data, but it also requires more effort in terms of tuning, understanding, and computational resources.

Conclusion

In data science, finding the best-fit model or technique is both a science and an art. While theoretical understanding and experience guide initial choices, trial and error remain an integral part of model development. Testing different algorithms, refining features, and validating model performance are all steps that lead to discovering the optimal solution.