What metrics should you prioritize when evaluating a data mining model?

Accuracy gives a quick snapshot of your model's correctness, but assessing error metrics like MAE, RMSE, or MSE provides deeper insights into prediction deviations and sensitivity to outliers, crucial for a comprehensive model evaluation.

As stated before, classification, clustering or regression errors are mostly measured as norms, i.e. average value of the individual absolute or square errors for training or test sets. However, higher order norms of the individual errors might be better to consider in some cases, like H-infinity norm where the maximum error matters more [1] [1] Lucas, C., Lesani, H. and Fereidunian, A., "Distribution systems reconfiguration using pattern recognizer neural networks", International Journal of Engineering, Vol. 15, No. 2, (2002), 135-144

Accuracy measures the average correctness of your model irrespective of your data balance. Errors on the other hand can help you to understand how well your model generalizes to new data and how much outliers could impact your model. It is common to use a combination of accuracy and error metrics to comprehensively evaluate a model's performance.

at first glance i look at the accuracy and loss graph and then other stuff like confusion matrix and correlation graph to make the model more accurate i prefer not to change the complex parameters generally but try only to fine tune the model using only epochs and batch size and the most important one that is learning rate

Accuracy is easy to interpret and provides a simple and intuitive measure of overall model performance, especially in binary classification. It is more suitable for datasets having balanced classes and can be misleading in the case of imbalanced datasets. While accuracy provides a general measure of model performance, error metrics like MAE and RMSE offer a more detailed and context-specific evaluation, taking into account the types of errors that are more critical for a particular application. Error metrics that account for false positives and false negatives, such as precision, recall, and F1 score, provide a more nuanced evaluation. It's very crucial to choose metrics that align with the goals of the specific data mining problem at hand.

Precision gauges how accurately your model identifies positives, while recall measures its completeness in capturing all actual positives. Crucial for binary classification, these metrics offer a balanced view of accuracy and coverage in model performance.

We use precision when we want to affirm the correctness of the model. It measures actual positive out of predicted positive samples. Precision is useful when the cost of false positives is very high such as in Marketing campaigns, where contacting customers who are not interested in the offer can result in financial loss. Recall measures predicted positive out of real positive. Recall is used when we want to avoid the false negative as much as possible. In high-risk disease detection cases (like cancer) recall is a more important evaluation metric than precision.

You've a task of sorting toys in 2boxes, for your fav toys and another for un-fav toys. Precision is how accurate you're when you claim a toy is favorite. It's the ratio of correctly identified favorites to all toys put in the "Favorites" box. High precision means you rarely put the wrong toys in the "Favorites" box. Recall is how good you're at finding all your true favorites. It's ratio of correctly identified favorites to all toys that should be in the "Favorites" box. High recall means you rarely miss identifying true favorites. There's often a trade-off between precision and recall. Being overly precise may cause you to miss some true fav while aiming for high recall might occasionally result in putting non-fav in the "Favorites" box

When it comes to binary classification on imbalanced data, precision and recall are good metrics to measure model performance. Ideally we would like to have both these metrics as high as possible. Now the issue here is, in real life, models don’t always perform well in both precision and recall. Here is where we choose one of them based on business priorities. In cases where business is fine with having a few false positives but they want to capture as many true positives as possible then recall is your go to metric. When false positives have greater implications to business and they are fine to let go of a few true positives then tuning for higher precision will do the trick

An expert opinion often underscores the significance of finding the right balance between these two metrics, as they inherently trade off against each other. Precision is crucial in scenarios where the cost of false positives is high. A high precision indicates a low rate of false positives, meaning the model is cautious when labeling instances as positive. Recall, on the other hand, becomes crucial when the cost of false negatives is substantial. High recall implies that the model is adept at capturing most of the relevant instances, minimizing the chances of overlooking important cases. Striking the right balance ensures a model that not only makes accurate predictions but also comprehensively captures all instances of interest.

I have seen people jump to F1 score as soon as they see that the data is imbalanced. It's not wrong to do so. But what one must realise is Precision and recall have their significance! If the data is imbalanced one's focus should not be just upon increasing the F1 but on satisfying the use case. A model to detect cancer or covid should have a high recall. It's a risk to tell that a cancerous patient is fine. On the other hand, an ad recommendation system should have a high precision. If more unrelated ads are shown more the user might lose interest. Bottomline - The use case is important and the metrics should be chosen based on that!

The F1 score is the harmonic mean of precision and recall. It provides a balanced measure that considers both false positives and false negatives. When the importance of FP (precision) and FN (recall) is roughly equal, F1 score serves as a suitable metric. if the importance of either one (FN or FP) is significantly greater than the other then we should focus on precision or recall accordingly. The ROC curve (Receiver Operating Characteristic) and the area under the ROC curve (AUC-ROC) are valuable when you want to explore the trade-off between sensitivity (recall) and specificity (true negative rate) at various classification thresholds. ROC curve can be extended to evaluate the performance of models in multiclass classification problems.

It is common for people to immediately turn to the F1 score when they encounter imbalanced data, and this approach is not necessarily incorrect. However, it is crucial to recognize the significance of precision and recall in such situations. When dealing with imbalanced data, the primary focus should not solely be on increasing the F1 score, but rather on meeting the requirements of the specific use case. For instance, a model designed to detect cancer or COVID-19 should prioritize high recall. Misclassifying a cancerous patient as healthy poses a significant risk. Conversely, an ad recommendation system should prioritize high precision. Displaying unrelated ads to users may lead to a loss of interest.

F1 Score is a way to measure classification model performance which gives equal weightage to recall & precision. Depending on use case you get into a situation recall is more important than precision i.e. cost of FN is higher than cost of FP. In such cases different variants of F score which gives different weightage to recall/precision. ROC curve is way to measure model generalization capabilities (True positive rate vs. False positive rate) i.e. measures model performance at various thresholds. Finally metric would always depend on business success criteria. But normally good way to look at is to choose a model with best ROC and then pick up a threshold that gives desirable recall & precision trade-off hence an acceptable F1 Score.

Different metrics can be useful depending on context, the ROC curve and F-1 score are probably useful most of the time providing insight and clear depiction

Lift assesses the effectiveness of targeting specific segments, comparing response rates to the overall average. Gain measures the cumulative percentage of positive instances captured when prioritizing by model scores. Both metrics offer insights into a model's ability to rank and target high-potential instances in ranking or scoring scenarios.

Lift: Think score multipliers. Each segment gets a lift score – how much more likely they are (think gold!) compared to the average. High lift? Jackpot! You found a segment swimming in potential. Gain: Picture sorting cards, gold first. Gain tells you how much "gold" you catch at each sorting step. 90% of your top 10% are VIP customers? That's high gain! You're prioritizing the most promising instances. Both metrics help you target like a laser, focusing efforts where they pay off most.

A lift value above 1 indicates the model's effectiveness in identifying segments with higher response rates. Gain, however, assesses how efficiently the model captures positive instances within a certain percentage of the dataset ranked by the model, aiding in resource allocation for targeted actions, such as marketing campaigns or customer retention strategies.

Lift is crucial in targeted marketing or customer segmentation models, particularly when resources for campaigns are limited. It helps in assessing the effectiveness of focusing on a top-ranked subset of individuals, showing whether targeting this group yields better results than a random approach. A high lift indicates efficient identification of the most responsive segment. Gain is important in applications like fraud detection or predicting rare events, where understanding early identification of positive cases is key. This metric evaluates how well the model identifies positive instances at different dataset segments, allowing you to optimize how much of the data to consider for capturing a substantial proportion of positive responses.

Lift measures the improvement or effectiveness of a model or campaign in comparison to a baseline or random selection. It quantifies how much better the model or campaign performs in targeting a particular segment compared to a random approach. Gain, on the other hand, measures the cumulative impact or benefit of a model or campaign over a period of time. It represents the incremental gain achieved by targeting specific segments compared to not targeting them. Gain is typically presented as a cumulative percentage of the total response or conversion achieved over a given period.

When it comes to evaluating a data mining model, don't forget about complexity and scalability. Complexity measures how tough and pricey it is to train, test, and deploy your model, considering factors like data size and algorithm intricacy. On the other hand, scalability gauges how well your model handles growing amounts of data without losing efficiency. These metrics are like the backstage crew ensuring your model's practicality and feasibility in the real world. So, while precision and recall take the spotlight, don't overlook the nuts and bolts of complexity and scalability for a well-rounded assessment.

Think of your data mining model as a race car. Complexity is the engine – powerful but fuel-guzzling? Simple yet sluggish? It depends on data size, algorithm, and even hardware. Scalability is the track – can it handle a few laps (small data) or a Grand Prix (massive datasets)? Scalable models can handle growth in data and users without chugging or crashing. Both metrics decide if your model is a real-world champion. A complex beast might win sprints, but a scalable workhorse dominates long-distance runs.

Complexity serves as a direct metric for evaluating various aspects of your model, including CPU usage, memory footprint, latency, and size. In contrast, scalability extends its scope to encompass the entire model pipeline, encompassing elements like the data pipeline and the model inference pipeline. Scalability specifically addresses the model's capacity to scale up or down in response to factors such as the volume of requests and the utilization of accelerators like GPUs or TPUs.

Complexity should also incorporate how difficult your model is to interpret or understand (by stakeholders, but even the data scientists themselves!). Regression and shallow decision trees are among the least complex (most interpretable), while neural networks are perhaps among the most complex (least interpretable). In general, this interpretability (lack of complexity) tends to be especially important for prescriptive use cases where the goal of the model is to identify what variables are driving another target variable. For predictive use cases, it’s often preferred to get the most predictive model even if it’s more complex (less interpretable), with the exception of more regulated use cases like credit modeling and the medical field.

Complexity gauges the cost and difficulty of managing your model, influenced by data size, parameters, and computational requirements. Scalability measures your model's ability to maintain performance as data volume grows, considering parallelization and distribution capabilities. These metrics provide insights into the practicality and feasibility of your model in real-world scenarios.

The evaluation metrics depend on the type of ML problem and the nature of the Business problem. There are different sets of metrics for Regression and Classification model types. For Regression ML problems we can use: Mean absolute error (MAE) Mean square error (MSE) Root mean squared error (RMSE), Root mean squared Log error (RMSLE) R squared Adjusted R Squared For Classification ML problems we can use: Accuracy Precision Recall AUC-ROC F1 score We can choose any of these based on how we have defined our business problem.

Apart from all the performance metrics, you might need some other metrics to focus on other objectives (including bias, fairness and more). Some specific models (e.g., recommender systems) also can be evaluated in many different ways based on different objectives (e.g., serendipity).

Understanding the distribution of the target variable is also important. When dealing with imbalanced data, where one class significantly outnumbers the other, prioritizing metrics like precision and recall becomes essential. Precision helps identify false positives, ensuring that positive predictions are genuinely accurate, while recall aids in capturing as many true positives as possible. Additionally, utilizing the area under the precision-recall curve (AUC-PR) can offer a comprehensive evaluation, especially when imbalanced classes challenge traditional accuracy measurements. Balancing the trade-off between precision, recall, and overall accuracy is key to a robust evaluation of a data mining model.

Statistical Analysis: - MAE - MSE - RMSE - R2 Classification - Accuracy - Precision - Recall - F1 Score - ROC AUC - Confusion Matrix - Log Loss (Cross Entropy Loss) Clustering: - Silhouette Score - Davies-Bouldin Index - Calinski-Harabasz Index Regression: - R2 - MAE - MSE - RMSE Association Rules: - Support - Confidence - Lift Deep Learning: - Accuracy - Precision - Recall - F1 Score - Loss Function - MAE - MSE - Categorical Cross-Entropy - ROC AUC - Intersection over Union (IoU) for object detection or segmentation Hyperparameter Tuning: - Cross-Validation Scores - Grid Search Performance - Learning Curves Data Drift Monitoring: - Kolmogorov-Smirnov test - Kullback-Leibler Divergence - Jensen-Shannon Divergence

Models should always be evaluated using a metric that is very close to the intended use. Meaning, the metric should approximate the impact of applying the model in the world. The impediment to doing this is you need to collect data points about the business case. This would include the estimated costs/benefits of every outcome for every decision the model would make. Without this level of detailed analysis you are only ever reporting abstract model performance that is disconnected from the use case.