Last updated on Mar 28, 2024

What criteria are used to evaluate Machine Learning model performance in a project workflow?

Machine learning (ML) is a process of creating and applying algorithms that learn from data and make predictions or decisions. However, how do you know if your ML model is performing well and meeting your project goals? In this article, you will learn about the criteria that are used to evaluate ML model performance in a project workflow.

1 Project objectives

Before you start building and evaluating your ML model, you need to define your project objectives. What are you trying to achieve with your ML model? What are the key performance indicators (KPIs) that measure your success? How will you use the model results in your business or domain? These questions will help you set the scope, expectations, and requirements for your ML project and guide your model evaluation strategy.

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Sanjay Kumar MBA,MS,PhD
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Before embarking on the creation and evaluation of a machine learning (ML) model, it's crucial to clearly define the project objectives. This involves determining what you aim to achieve with your ML model and identifying the key performance indicators (KPIs) that will gauge its success. Additionally, understanding how the model's results will be applied in your specific business or domain context is vital. Addressing these questions helps in setting a clear scope, establishing expectations, and outlining the requirements for your ML project. This clarity also guides your strategy for evaluating the model, ensuring that it aligns with the intended goals and delivers practical, measurable outcomes.

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2 Data quality

Another important criterion for evaluating ML model performance is the quality of the data that you use to train and test your model. Data quality refers to the accuracy, completeness, consistency, and relevance of the data for your project objectives. Poor data quality can lead to unreliable, biased, or overfitted models that do not generalize well to new or unseen data. Therefore, you need to perform data cleaning, preprocessing, and exploration steps to ensure that your data is suitable for your ML model.

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Sanjay Kumar MBA,MS,PhD
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Evaluating the performance of a machine learning (ML) model heavily relies on the quality of the data used for training and testing. Data quality encompasses aspects such as accuracy, completeness, consistency, and relevance to the project objectives. If the data quality is poor, it can result in models that are unreliable, biased, or overfitted, leading to poor performance on new or unseen data. To mitigate these risks, it's essential to engage in thorough data cleaning, preprocessing, and exploration.

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3 Model metrics

Once you have prepared your data and built your ML model, you need to measure how well your model performs on the data. Depending on the type and complexity of your ML model, you can use different metrics to evaluate its performance. For example, if you have a regression model that predicts continuous values, you can use metrics such as mean absolute error (MAE), root mean squared error (RMSE), or coefficient of determination (R^2) to quantify the error or variance of your model predictions. If you have a classification model that predicts discrete labels, you can use metrics such as accuracy, precision, recall, f1-score, or confusion matrix to assess the correctness or completeness of your model predictions.

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Karen Haigh

Consultant for Cognitive Electronic Warfare and Embedded AI/ML ◆ Speaker, Author ◆ IEEE Fellow
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Common metrics for classification tasks are accuracy, precision, recall, F1 score, false-positive rate and false-negative rate. None of these meet the needs of deployed systems because they don't account for the costs of different mistakes. For example, in an explosives-detection task for airport security, a 99% accuracy means nothing. For the 3 million passengers travelling through US airports every day, that's 30,000 mistakes. Are those mistakes false positives that incur extra screening, or false negatives that lead to possibly hundreds of lives lost? Instead of the common metrics, assess each type of mistake in the confusion matrix against its cost. Note that costs can change as a function of risk.

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Nader Karballaeezadeh

Transportation Engineer | Pavement Engineer | Researcher & Reviewer | Machine Learning | Python
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Adopting the performance indexes in order to evaluate the applicability of a model is a challenging task for data analysts. There is a vivid fact about this matter that routine indices like R2, MAE, and MSE are not definite criteria to judge the model. Sometimes, a model has a poor R2, however, it outputs good MAE and MSE errors. It means the model might have a satisfying level of performance, but it comes from us how to show its power.

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4 Model validation

However, measuring the performance of your ML model on the same data that you used to train it is not enough. You also need to validate your model on new or unseen data that reflects the real-world scenarios that you want to apply your model to. Model validation is a process of splitting your data into training, validation, and testing sets, and using them to train, tune, and evaluate your model respectively. This way, you can avoid overfitting or underfitting your model to the data and ensure that your model can generalize well to new data.

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5 Model comparison

Another criterion for evaluating ML model performance is to compare different models or variations of the same model to find the best one for your project objectives. Model comparison is a process of applying different algorithms, parameters, features, or techniques to your data and measuring their performance using the same metrics and validation methods. This way, you can identify the strengths and weaknesses of each model and select the one that has the highest performance or the lowest complexity.

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6 Model interpretation

Finally, you need to evaluate how well you can interpret and explain your ML model and its predictions. Model interpretation is a process of understanding how your model works, what factors influence its predictions, and how confident or uncertain it is about its predictions. Model interpretation is important for validating, debugging, and improving your model, as well as for communicating and trusting your model results. Depending on the type and complexity of your model, you can use different methods to interpret your model, such as feature importance, partial dependence plots, or SHAP values.

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7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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Machine Learning

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What criteria are used to evaluate Machine Learning model performance in a project workflow?

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2

3

4

5

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1 Project objectives

2 Data quality

3 Model metrics

4 Model validation

5 Model comparison

6 Model interpretation

7 Here’s what else to consider

Machine Learning

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What criteria are used to evaluate Machine Learning model performance in a project workflow?

1

2

3

4

5

6

7

1 Project objectives

2 Data quality

3 Model metrics

4 Model validation

5 Model comparison

6 Model interpretation

7 Here’s what else to consider

Machine Learning

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