What are common misconceptions about ML model evaluation?

1 Misconception 1: Accuracy is the best metric

One of the most widely used metrics for ML model evaluation is accuracy, which measures the proportion of correct predictions over the total number of predictions. However, accuracy is not always the best metric, especially for imbalanced or skewed datasets, where one class dominates the others. In such cases, accuracy can be misleading, as it does not reflect the true performance of the model on the minority classes. For example, if a model predicts that 99% of the patients are healthy and only 1% are sick, and the actual distribution is the same, the accuracy is 100%, but the model is useless for diagnosing the sick patients. A better metric for imbalanced datasets is the F1-score, which combines precision and recall, and balances the trade-off between false positives and false negatives.

2 Misconception 2: Training error is a good indicator

Another common misconception is that the training error, or the error that the model makes on the training data, is a good indicator of the model's performance. However, this is not true, as the training error can be very low or even zero, but the model can still perform poorly on new or unseen data. This is because the model can overfit the training data, meaning that it learns the noise or specific patterns of the training data, but fails to generalize to new data. To avoid overfitting, it is important to use a validation set, which is a subset of the data that is not used for training, but for tuning the model's hyperparameters and selecting the best model. The validation error, or the error that the model makes on the validation set, is a better indicator of the model's performance than the training error.

3 Misconception 3: More data is always better

A third misconception is that more data is always better for ML model evaluation. While it is true that more data can help improve the model's performance and reduce overfitting, it is not always the case that more data is available or feasible. Sometimes, collecting more data can be costly, time-consuming, or impractical, and adding more data can also introduce noise, outliers, or biases that can affect the model's performance. Therefore, it is not enough to just have more data, but also to have good quality data that is relevant, representative, and consistent. Moreover, it is also important to use appropriate data preprocessing techniques, such as cleaning, scaling, encoding, and feature engineering, to transform the data into a suitable format for the model.

4 Misconception 4: Cross-validation is always necessary

A fourth misconception is that cross-validation is always necessary for ML model evaluation. Cross-validation is a technique that splits the data into k folds, and uses each fold as a validation set while training the model on the remaining k-1 folds. This way, the model is evaluated on different subsets of the data, and the average error across the k folds is used as the final error estimate. Cross-validation can help reduce the variance and bias of the error estimate, and provide a more robust and reliable evaluation. However, cross-validation is not always necessary, especially when the data is large enough to have a separate and sufficient validation set. Cross-validation can also be computationally expensive and time-consuming, as it requires training and evaluating the model k times. Therefore, cross-validation should be used with caution and discretion, depending on the data size, availability, and complexity.

5 Misconception 5: A single metric is sufficient

A fifth misconception is that a single metric is sufficient for ML model evaluation. However, this is not true, as different metrics can capture different aspects of the model's performance, and a single metric can be insufficient or misleading. For example, accuracy can be high, but precision or recall can be low, depending on the model's tendency to make false positives or false negatives. Similarly, a low mean squared error (MSE) can hide large errors in some predictions, while a high R-squared can mask the variability or heterogeneity of the data. Therefore, it is advisable to use multiple metrics, and compare them with the baseline or benchmark models, to get a more comprehensive and holistic view of the model's performance.

6 Misconception 6: Evaluation is the final step

A sixth misconception is that evaluation is the final step in the ML model development process. However, this is not true, as evaluation is an iterative and continuous process that should be done throughout the model development cycle. Evaluation can help identify the strengths and weaknesses of the model, and provide feedback and guidance for improving the model. Evaluation can also help monitor the model's performance over time, and detect any changes or anomalies that can affect the model's reliability and accuracy. Therefore, evaluation is not a one-time activity, but a recurring and essential component of the ML model development process.

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?