Image by Author | Midjourney & Canva
Introduction
Choosing the right machine learning model for your data is of major importance in any data science project. The model you select will have a significant impact on the insights you derive from your data, and ultimately determine the usefulness of a project. In this article, we aim to provide practical tips to help new practitioners make informed decisions when choosing machine learning models.
1. Understand Your Data
Understanding the type and structure of your data is the foundation of model selection. Data can be numerical, categorical, text-based, or image-based, each requiring different preprocessing and modeling techniques. For instance, numerical data might be well-suited for regression models, while categorical data might require encoding before being used in a machine learning algorithm. Beyond this, different models have different levels of ability for dealing with, for instance, missing data. So before looking at which models you might want to use for a project, knowing which models have even a fighting chance of performing well with your data is essential.
Not only is having data important, having quality data is crucial for model performance. Cleaning and preprocessing your data involves handling missing values, removing duplicates, and normalizing or standardizing features. High-quality data enables models to learn better and make more accurate predictions.
If you have used Python and popular libraries such as Scikit-learn and Pandas in the past for data preprocessing, code such as the following will not be unfamiliar to you.
|
import pandas as pd from sklearn.preprocessing import StandardScaler
# Load numerical dataset data = pd.read_csv(‘data.csv’)
# Remove missing values data.dropna(inplace=True)
# Srandardize particular features scaler = StandardScaler() data[[‘feature1’, ‘feature2’]] = scaler.fit_transform(data[[‘feature1’, ‘feature2’]]) |
Takeaway: Understand your data’s type and quality for better model selection
2. Define the Problem Clearly
Clearly defining your problem helps in selecting the appropriate model. Problems in machine learning typically fall into categories like classification, regression, clustering, etc., and knowing where your problem fits will inform your choice. For example, predicting whether an email is spam or not is a classification problem, while predicting house prices is a regression problem. You likely already know this, but it’s worth consciously putting it front of mind when selecting your choice of algorithm to avoid poor fit and unecessary extra work.
Similarly, setting clear, measurable goals is essential. Determine what success looks like for your model: are you aiming for high accuracy, low error rates, or specific precision and recall metrics? Clear objectives guide your model selection and evaluation criteria.
Takeaway: Clearly state your problem and goals to guide model selection
Tip 3: Start Simple
Like most other things in life, the process of choosing the proper machine learning model should start slowly. It’s best to begin with simple models like linear regression or logistic regression, depending on your problem type of course. These models are easy to understand, quick to train, and often provide a solid baseline performance. Starting simple helps you establish a benchmark and understand the fundamental relationships in your data. If you don’t know the performance of the simple models on your data, how will you know if the trouble of pursuing more complex models is worthwhile?
Once you have a baseline, you can experiment with more complex models. Gradually increasing model complexity allows you to build on your foundational understanding and determine if more sophisticated models provide significant improvements.
Making simple models even more attractive is that they are generally relatively simple to build. Compare the following few lines of Python necessary for building a linear regression model versus something such as a complex ensemble or a neural network architecture. Not only would they take much more time to build and train, interpreting results would be a whole different ballgame.
|
from sklearn.linear_model import LinearRegression
# Starting with a simple linear regression model as a baseline model = LinearRegression() model.fit(X_train, y_train) baseline_predictions = model.predict(X_test) |
Takeaway: Start with simple models to establish a solid performance baseline
4. Evaluate Multiple Models
Now that we have a baseline, it’s time to add complexity, and increase the number of candidate models. Comparing these multiple models undoubtedly helps you find the best fit for your data. Use metrics such as accuracy, precision, recall, and F1 score to evaluate performance, depending on your given situation. Each metric provides different insights into model performance and helps you make a more informed decision. Comparing multiple metrics across models can provide some of the best insights, even if you make your selection based primarily on one of them.
Cross-validation is a technique to assess the robustness of your model. It involves dividing your data into multiple folds and training and testing the model on different combinations of these folds. Techniques like k-fold and stratified cross-validation help ensure your model’s performance is consistent across different subsets of the data. It’s this subset concept that provides the real benefit of cross validation, as well as not “wasting” any of your data when it comes to training.
Takeaway: Evaluate multiple models and use cross-validation for robust performance
5. Consider Computational Resources
How much computational power and time would be needed to traing your models? This is obviously an oimporant consideration. Need the model tomorrow, but the neural network architecture you want to use would take significantly longer to train? Would the cost of renting GPUs in the cloud cause your project to go over budget? If resources are limited, simpler models might be more practical. Modeling is an exercise in trade-offs: precision vs. time and cost. Sometimes “good enough” is good enough, while others the additional training time and cost is warranted. Your model choice will be impacted as a result.
Also, think about the deployment environment. For instance, models that are resource-intensive may not be suitable for real-time applications. Choose models that balance performance with computational efficiency, ensuring they can be effectively deployed and scaled as needed.
Takeaway: Balance model complexity with available computational resources and deployment needs
Final Thoughts
In summary, choosing the right machine learning model involves these important considerations: understanding your data, defining your problem clearly, starting with simple models, evaluating multiple models, and considering computational resources. Each of these steps plays a crucial role in ensuring you select the most appropriate model for your data.
Model selection is an iterative process that improves with experience. The best way to improve in the model selection game is to continually experiment with different models and learn as you go. It goes without saying, but the more you practice, the better you’ll become at choosing the right model for your data.