Machine learning (ML) models are powerful tools that can learn from data and make predictions or decisions. However, how do you know if your ML model is performing well and meeting your expectations? In this article, you will learn how to evaluate the effectiveness of ML models using different metrics and methods.
The first step to evaluate the effectiveness of ML models is to define the goal of your model and the problem you are trying to solve. Depending on the type and complexity of your ML problem, you may have different criteria and objectives for measuring the success of your model. For example, if you are building a model to classify images, you may want to optimize the accuracy and precision of your predictions. If you are building a model to recommend products, you may want to maximize the revenue and customer satisfaction.
You can assess a model's effectiveness through various metrics like accuracy, precision, recall, and F1 score. Cross-validation helps check performance across different data subsets. AUC-ROC curves are handy for binary classification. Also, keep an eye on overfitting or underfitting by comparing training and validation performance.
In evaluating machine learning models, key metrics for an image classification model include accuracy, precision, recall, and the F1 score, which balances precision and recall. Additionally, the Area Under the Receiver Operating Characteristic Curve (AUC-ROC) is crucial for assessing the model's ability to differentiate between classes. For a product recommendation model, important measures would focus on revenue, customer satisfaction, and engagement metrics like click-through and conversion rates. These metrics help gauge the real-world impact and effectiveness of the model.
Si Linkedin quiere que expertos/as en diferentes áreas TRABAJEMOS evaluando la validez global y/o específica de diferentes artículos creados con IA, lo primero que Linkedin debería hacer es establecer una REMUNERACIóN por ese trabajo. El trabajo de los/as profesionales DEBE ser remunerado o no será.
At present, the effectiveness of ML models is first depending on the data acuracy corresponding to the right samples and the sampling methods.
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Identification of the problem domain is crucial for effective selection of model. Major two major division on classical machine learning models are : supervised (working with labelled data) and unsupervised (working with un-labelled data).
Choosing the right metrics to quantify the performance and quality of your ML model is the next step. Metrics are numerical values that compare your model's predictions with actual outcomes or labels. There are many types of metrics for different ML problems, such as classification, regression, clustering, and ranking. Common metrics include accuracy, precision, recall, F1-score, mean squared error (MSE), root mean squared error (RMSE), R-squared, mean average precision (MAP), and normalized discounted cumulative gain (NDCG). Accuracy measures the proportion of correct predictions among all predictions. Precision is the proportion of correct positive predictions among all positive predictions. Recall is the proportion of correct positive predictions among all actual positive outcomes. F1-score is the harmonic mean of precision and recall. MSE is the average of the squared differences between the predictions and the actual values. RMSE is the square root of the MSE. R-squared is the proportion of the variance in the actual values that is explained by the model. MAP is the average of the precision values at different recall levels. Lastly, NDCG is a measure of ranking quality based on relevance and position of items.
It's vital to remember that a blend of metrics is often necessary, as each has its strengths and limitations. For instance, accuracy might be less informative for imbalanced datasets, where precision and recall offer more insights. In regression, while MSE and RMSE quantify error magnitude, R-squared gives a sense of the model's explanatory power. For applications like recommendation systems, MAP and NDCG are more nuanced, capturing aspects like relevance and ranking quality. Ultimately, the choice of metrics should align with your specific model goals and the nature of the data
One of interesting fact which shall help you relate core statistics to ML based KPIs. The common metrics like precision, recall are built around the elements of the confusion matrix which are basically trying to optimise type I or type II errors. Lets say, we want to optimise precision implies trying to reduce false positive (instances that are incorrectly classified as positive) analogous to reducing type I error (we reject the null when it's true, model rejected genuine/good transactions when it should be allowed). Similarly, recall deals with false negative/type II error (model allowed fraudulent/bad transactions when it should be rejected/challenged). Again type I and II are inversely related, finding balance is the task, optimise F1.
I agree with this view as it is well established and tested with time. Quality measurement has alwase been a preferred factor and most desirable issue.
After defining the goal, I think two missing steps are pivotal for successive ML models: 1. *Literature review* will help you to learn from great minds' knowledge and mistakes. It will provide a deep understanding of existing methodologies, advancements, and potential pitfalls within the chosen domain. This step not only informs the selection of appropriate algorithms but also aids in refining research questions and identifying gaps in current knowledge. 2. *Cleaning of data*. Clean and well-structured data substantially enhances model accuracy and mitigates biases, ensuring the reliability and generalizability of the machine learning outcomes.
Selecting appropriate metrics is pivotal in evaluating machine learning model efficiency. Tailor metrics to the specific task, considering factors like precision, recall, and F1-score for classification, or Mean Squared Error for regression. Align metrics with the desired outcome; for example, prioritizing sensitivity in medical diagnoses. Regularly reassess metrics based on project goals and user requirements. By choosing metrics that resonate with the task at hand, you establish a reliable framework for assessing the model's performance and ensuring it aligns optimally with the intended objectives.
The third step is to split your data into different subsets for training, validation, and testing your ML model. Training data is used to fit your model and adjust its parameters. Validation data is used to tune your model's hyperparameters and select the best model among different options. Testing data is used to evaluate the final performance of your model on unseen data. The typical ratio of splitting the data is 70% for training, 15% for validation, and 15% for testing. However, this may vary depending on the size and characteristics of your data.
Selecting a well-considered validation set is essential to avoid model failure in real-world applications. This set should reflect future scenarios, with time series data needing the most recent segments. It's also crucial to account for differences that new data might introduce, like unseen individuals or categories.
In ML, data splitting should be tailored to the dataset's size and complexity. For large datasets, a smaller training set can suffice, allowing more data for validation and testing. With smaller datasets, k-fold cross-validation is effective, utilizing the entire dataset for both training and validation. Additionally, stratified splitting is essential for maintaining class distribution, especially in imbalanced datasets. This ensures the model's performance accurately reflects its generalization capabilities
A key step in ML is splitting your data into training, validation, and testing sets. Typically, it's 70% for training, 15% each for validation and testing. This split helps in fitting, tuning, and evaluating your model. But it's not one-size-fits-all. If your data has class imbalance, techniques like stratified sampling can ensure each class is adequately represented. In time-series analysis, consider a time series split or a blocking time series split to maintain the temporal order of data. These methods prevent future data from leaking into the training set, crucial for predictive accuracy. Remember, the right split depends on your data's unique characteristics.
If data includes known values for the label, we can use this to train a classifier so that it finds a statistical relationship between the features and the label value; but how will we know if our model is any good? How do we know it will predict correctly when we use it with new data that it wasn’t trained with? It is best practice to hold out some of your data for?testing?in order to get a better estimate of how your models will perform on new data by comparing the predicted labels with the already known labels in the test set.
Evaluating machine learning model efficiency is enhanced by strategic data splitting. Divide the dataset into training, validation, and test sets to assess performance accurately. Train the model on one subset, validate on another for parameter tuning, and finally, test on an independent set for unbiased evaluation. This systematic approach prevents overfitting and provides a robust measure of the model's generalization capabilities. By judiciously splitting the data, you ensure a reliable evaluation process, essential for gauging the efficiency of machine learning models with precision.
The fourth step is to apply cross-validation to reduce the variance and bias of your model's performance. Cross-validation is a technique that involves dividing your data into k folds and using each fold as a testing set while using the rest as a training set. This way, you can obtain k estimates of your model's performance and average them to get a more reliable and robust measure. Cross-validation can also help you avoid overfitting, which is when your model learns too much from the training data and fails to generalize to new data.
According to Refaeilzadeh, Cross-Validation is a statistical method of evaluating and comparing learning algorithms by dividing data into two segments: one used to learn or train a model and the other used to validate. However, this is not only used for model evaluation, since its characteristics help us in the process of model selection. There are many paper in this area, such as Pinto et.al (2019). Refaeilzadeh, P., Tang, L., Liu, H. (2009). Cross-Validation. In: LIU, L., ?ZSU, M.T. (eds) Encyclopedia of Database Systems. Springer. Pinto, J. M., & Mar?al, E. F. (2019). Cross-validation based forecasting method: a machine learning approach.
Applying cross-validation is a key strategy for robustly evaluating machine learning model efficiency. Techniques like k-fold cross-validation involve splitting the dataset into multiple folds, training the model on different subsets, and averaging performance metrics. This minimizes the risk of overfitting or underfitting to a particular subset. Cross-validation provides a more comprehensive assessment of the model's generalization across different data partitions, enhancing reliability. By incorporating cross-validation into the evaluation process, machine learning experts ensure a more thorough and accurate understanding of their model's efficiency and predictive capabilities.
In the process of selecting a validation strategy, it’s crucial to ensure that the observations are independent of each other. This becomes relevant when dealing with time series data. Employing K-fold cross-validation in such cases may yield overly optimistic outcomes, primarily because this approach ignores the temporal structure of the data. Ideally, the data for model training should temporally precede the validation data. However, with k-fold cross validation, the data is "mixed up", leading to folds where future observations are used for training or past observations for testing. A more realistic outcome can be achieved with a validation strategy that respects the temporal structure of the data, e.g. rolling or expanding window.
The fifth step is to compare the performance of different ML models using the metrics and methods you have chosen. You can use various tools and frameworks to implement and compare different ML models, such as scikit-learn, TensorFlow, PyTorch, and Keras. You can also use visualization techniques, such as confusion matrices, ROC curves, and learning curves, to analyze and interpret the results of your models. You should select the model that best meets your goal and satisfies your constraints, such as time, cost, and complexity.
If we compare classification tasks, we could be using accuracy, precision, recall, and the F1 score. For regression tasks, we may use mean squared error (MSE; keeping in mind the sensitivity to outliers) and mean absolute error (MAE). Note: The choice of metric should align with the specific objectives and requirements of the model's application (e.g., what I want to do and achieve).
To compare models one can use the initial chosen metrics such as accuracy , specifity ,precision, RMSE etc to see how well a model peforms.
Efficient evaluation of machine learning models involves rigorous comparison. Employ metrics relevant to your goals, such as accuracy, precision, or AUC-ROC. Consider computational efficiency and scalability. Utilize statistical tests to validate performance differences. Assess models across diverse datasets to ensure robustness. By systematically comparing models, machine learning practitioners can pinpoint the most effective solution for their specific objectives, fostering data-driven decision-making and advancing model efficiency.
The last step is to test your selected model on the testing data that you have reserved for evaluation. This is the final and most important step to assess the effectiveness of your ML model and its ability to generalize to new and unseen data. You should report the performance of your model using the metrics you have chosen and compare it with the performance on the training and validation data. You should also check for any errors, anomalies, or biases in your model's predictions and address them if possible.
External validation of the model is important, going beyond output audit and duplication. Worthwhile trying a different modelling approach to compare notes and results including sensitivity analysis. Sometimes implementation of the same modelling approach in different languages R vs. Python can also lead to nuanced discrepancies which are often minor, but it's fascinating to figure out the reasons for the minor differences.
An often overlooked but essential 7th step involves testing the model on data from later time steps, particularly significant in domains such as healthcare where seasonal factors and temporal variations can influence outcomes. This proactive approach ensures the model's robustness and adaptability to evolving trends, thereby enhancing its real-world applicability and reliability.
I believe doing some form of residual analysis or checking where you are doing mistakes is also important. Looking beyond the metrics over the test set and focusing on - misclassification for a particular class, underfitting wrt certain features, or in time series: not being able to capture certain impulses or trends. It allows you to understand what are the limitations for the model and where it will or will not work. This is a bit tedious but an important part for getting beyond check test error metrics as it gives you more granular insights.
While it's typical to have a test dataset to your model, try to have various datasets to really test the robustness of your models. The more diverse, the better. Also if you have sensitive information, make sure to test your model's output across different demographic groups. You might identify biases in your predictions that you did not think of before. Let's aim for fairness and equity to stay away from unfair outcomes.
Classification: Accuracy, precision, recall, F1-score, ROC-AUC, confusion matrix. Regression: Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), R-squared. -Techniques like k-fold cross-validation help estimate the model's performance on unseen data. -Plotting performance metrics against training set sizes can reveal if the model would benefit from more data. -To find the optimal model complexity, assess the balance between underfitting (high bias) and overfitting (high variance). -Evaluating the model on a separate test set not used during training helps gauge its generalization capability. -Ultimately, the model’s effectiveness might also be measured by its impact on business KPIs or objectives.
Interpretability is crucial in evaluating ML models, offering transparency into decision-making. Understanding how a model reaches conclusions is essential for validation, trust, and addressing potential biases. For enhanced understanding, consider exploring methods like LIME (Local Interpretable Model-agnostic Explanations) or SHAP (SHapley Additive exPlanations), which provide insights into individual predictions, aiding effective model assessment. Or look at visual tools like Partial Dependence Plots (PDP) and Accumulated Local Effects (ALE) plots. These techniques offer valuable insights into model behavior, aiding in the effective assessment of its performance.
First of all, one needs to predefine what the model is to be used for. Are we in an exploratory or predictive setting. Once this is clear, one may rely on appropriate measures. However, typical statistical performance indicators such as confusion matrix, prediction r-squared, etc. are rarely what the stakeholders need. Over the past 30 years, I have had to devise custom measures to suit the client's needs.
addressing biases and fairness in models is pivotal. Using fairness metrics, like demographic parity or equalized odds, aids in detecting and mitigating biases, fostering inclusive AI. Interpretability holds immense significance, especially in sensitive domains. Employing transparent models or explanation techniques such as SHAP values or LIME enables us to elucidate model decisions. This transparency enhances trust, allowing stakeholders to comprehend and validate predictions, crucial for compliance and ethical AI adoption. By prioritizing fairness evaluations and interpretability techniques, we not only enhance model performance but also uphold ethical standards, fostering responsible AI deployment across varied applications.
The various questions above are of course important, as are the comments from the community, but I think there's one important thing to stress. A model with a satisfactory one-shot evaluation (on a fixed dataset) is only part of the story. All too often, the story ends there, but in the real world of business, it's important to keep a constant watch to determine whether there are any drifts such as Data drift (Changes in Data distribution) or Model drift (Changes in the function 'Features imply Labels'). MLOps is therefore something that should not be overlooked when deploying models in companies (for structural implementation, of course, but it can sometimes be avoided for a one-shot project).