How do you evaluate the performance and accuracy of your machine learning models?

In machine learning projects, splitting the dataset into training and test sets is a method used to assess how accurately the model performs on unseen test data. This approach aims to evaluate the model's performance on data it has never encountered, providing insights into whether the model has learned the underlying patterns of the data. Additionally, techniques like k-fold cross-validation and hyperparameter optimization can be employed. Using these methods, machine learning practitioners can gain comprehensive understanding of their model's capabilities, ensure generalization to new data, and fine-tune the model for optimal performance.

The size of the training set is really important for learning performance in a model. It should be enough for representing the data especially while evaluating the model performance. Because of the reason the training data size should be taken into considertion and the model metrics should be analysed according to the different size of data.

The independence of the training and test set is key to ensure a fair model evaluation ("no data leakage from train to test"). For independent identically distributed data, random sampling without replacement is sufficient. However, datapoints might be dependent: One common dependence structure is time series data: If the idea is e.g. to forecast a time series, the sample should be split not randomly, but in time: The training set should contain only observations before a certain cutoff time, and the test set only observations after this cutoff time. Otherwise, future information (that is unavailable to the model today) would be used in forecasting. Methods to deal with this issue are e.g. expanding/rolling window estimation.

A common mistake is information leakage from the test set to the trading process. It's important to have a holdout set that exactly replicates your generalization requirements. For example when working with videos, if you expect to have new videos at inference time, you must replicate that condition, especially if your model works on single frames of the video (adjacent frames included in the training set will make inference trivial). Another example, is when test sets are used at inference time to evaluate model validity. If any program logic relies on the test set, a holdout set is required to evaluate the overall performance.

The first question we need to ask is whether the model is successful. The second aspect to address is the optimization of the model's parameters. When making predictions, it's quite natural to encounter errors. For regression problems, the metrics to examine include MSE , RMSE, MAE which measure how close the observed and predicted values are. In classification problems, on the other hand, the key metrics derived from the confusion matrix are accuracy, precision, recall, and F1 score.For example, If the model fails to identify a fraudulent transaction as a fraud in the detected class, we would encounter an error. In this case, For sensitive measurements, we don't rely on a single model validation metric.

For classification tasks it's important to quantify what the goal is in your domain and problem. F1 score is a popular 1 number summary of classification performance that equally weights in the harmonic mean of precision and recall (the harmonic mean is non linear and penalized more by the smaller number). However in some contexts recall is more important (variable screening), in others precision is more important (any situation where false positives are devastating). We can't use recall alone since the trivial model gets perfect recall, and precision alone is inadequate. Tuning a metric such as the F-beta score to your problem allows quantifying decisions that are often made intuitively when models are indistinguishable by simpler metrics.

Baselines and benchmarks are commonly used in the fields of artificial intelligence and machine learning. They are important tools for developing new models and improving existing ones. Baseline are used as a reference point to evaluate the performance of more complex models. For example, the performance of models trained on data, such as decision trees, neural networks, is compared with the baseline. This helps determine whether the complexity of advanced models results in a significant improvement over the baseline. Benchmarks are typically standard tests or datasets used to measure the performance of a specific task. This allows for the comparison of different models in a standardized way.

Some metrics have baselines built in, such as R-squared which is negative if your model is worse than predicting the mean of the training data.

In machine learning, the model validation method varies depending on the type of problem. Holdout and K-Fold Cross-Validation are generally the first ones that come to mind. In addition to these, methods like Time Series Split and Bootstrapping are also other examples. These validation techniques help objectively assess the performance of the model on real-world data. To ensure further, various public surveys and tests can be conducted. If improvements are desired on the model, optimization techniques are employed.

A data-centric approach to ML shifts emphasis away from algorithm optimization or evaluation techniques. It is the quality and preparation of the data that models' learn from most greatly impact their performance. High quality, properly processed data are vital to ensuring more relevant, and representative results. Comprehensive data processing, EDA, and stratification are what enhance models’ robustness, reduces biases, and increases generalizability. Data-centric ML challenges the notion that complex algorithms are always necessary for high performance. In some cases, a well-prepared data can often suffice. Prioritizing data quality, EDA, and appropriate stratification result in more accurate, reliable, and explainable models.