How can you use accuracy as an evaluation metric?

Accuracy refers to the measurement of how well a machine learning model correctly identifies and classifies different features or patterns within medical images. It's a critical metric used to evaluate the performance of algorithms designed to assist medical professionals in diagnosing conditions and diseases from medical images such as X-rays, MRIs, and CT scans. For instance, if a model is developed to detect tumors in medical images, accuracy measures the percentage of correctly identified tumors compared to the total number of tumors present. In this scenario, high accuracy indicates that the model is effective in recognizing tumors accurately, which is crucial for providing accurate medical diagnoses.

Accuracy is a commonly used evaluation metric that measures the overall correctness of a model's predictions. Accuracy is helpful when the classes are balanced and misclassifications have equal consequences. However, accuracy may not be suitable in certain scenarios. One such scenario is when class imbalance exists. In such cases, a model that predicts the majority class for all instances could achieve a high accuracy, but fail to capture the minority class. Another scenario is when the costs of false positives and false negatives differ. For instance, in medical diagnoses, a false negative (missed diagnosis) could have severe consequences, making accuracy an inadequate metric as it does not differentiate between types of errors.

When dealing with imbalanced multiclassification tasks, Quadratic Weighted Kappa (QWK) is a more robust metric than accuracy, especially for ordinal data. E.g., in medical diagnosis, when classifying the severity of an illness from mild to moderate to severe. In such situations, QWK is unaffected by class imbalance and rather considers the degree of disagreement between the predicted and actual labels to determine the model's performance beyond just guessing randomly. To do this, it gives different penalties to different types of disagreements; the further away the predicted label is from the actual label, the higher the penalty. E.g., if the actual label was 3, a prediction of 5 would be penalised a lot more than a prediction of 2 or 4.

Accuracy is useful only if the data prepared for training purpose is treated well for the domain where we are leveraging this machine learning solution. For example we quite often face the challenge of high dimension of categorical feature in health care.

Accuracy is not a good metric for imbalanced data. For such cases, other metrics are more useful eg. Precision and recall. Recall minimises possibly of false negative. Precision minimises possibility of false positive

The definition of accuracy is deceptively simple. We have to be clear about how we count instances and true positives/negatives. Imagine needing to determine the number of cats in an image, up to 10 cats. We can solve this with a classifier that has 11 classes, representing numbers from 0 to 10. Now imagine an image with 8 cats preset. If our model predicts 7 cats, the default classifier "accuracy" computation in machine learning frameworks would consider this to be one incorrect prediction for one instance (image). But for this model we might want to consider this prediction to have 7 true-positives and one false-negative out of 8 instances (cats), to better reflect how close the model got to a completely correct result.

When datasets are imbalanced, it means one class significantly outnumbers the other. In such scenarios, accuracy can be a deceptive metric. Consider a diagnostic tool evaluating a rare disease present in just 1% of the population. If the tool always predicts "no disease" for every individual, it would still achieve a 99% accuracy rate, despite failing to identify any actual cases of the disease. Such a high accuracy in this context is not indicative of a useful model. For imbalanced datasets, it's crucial to look beyond mere accuracy and consider other metrics that account for the minority class, ensuring that the model's effectiveness isn't overshadowed by the majority class.

Hi, imagine we have a target variable which has 95% of outcomes as - “No” and 5% of outcomes as “Yes” and you build a model to predict “Yes” class, say your model predicts all “No”s accurately - meaning your model achieves 95% accuracy - would you still use accuracy as a metric for your model performance??? And do you think your model is accurate??

I would also recommend using ROC curves along with the accuracy metric. That will give you a better understanding of your model’s performance.

Limitations of Accuracy: 1. Accuracy metric could not be used with imbalanced dataset, it can give us misleading high accuracy figures.Hence it is not informative with multi-class problems 2. Does not account for false positives (Type I errors) and false negatives, For instance, in medical diagnosis, a false negative (saying someone is healthy when they're not) can have more serious consequences than a false positive (saying someone is sick when they're not). Accuracy treats all errors equally.

A fundamental machine learning concept, which checks whether they correctly predict class labels during inference. This concept is known as the Probability of Correct Classification (PCC). PCC is essential evaluate the pre-trained model's accuracy against your custom model architectures to confirm prediction alignment. It's noteworthy that accuracy can mislead in imbalanced datasets. This can inflate accuracy while compromising overall precision, leading to incorrect predictions. PCC emerges as a robust, reliable metric. However, it has limitations like sensitivity to outliers and computational demands. These constraints can be alleviated by dimensionality reduction, combining PCC with metrics like RTOL delta and ATOL delta.

Another this to keep in mind is that does not provide insights into the type of errors a model makes. It treats all misclassifications equally, regardless of their impact. Furthermore, accuracy disregards the inherent costs associated with different types of mistakes, which might be crucial in applications such as medical diagnoses or fraud detection. Therefore, a more comprehensive evaluation approach should consider these factors and incorporate domain-specific considerations to provide a holistic assessment of a model's effectiveness.

High accuracy doesn't necessarily guarantee a reliable model. A high accuracy score could be achieved by a model that learns to predict the dominant class for all cases, ignoring the minority class entirely. This can lead to false negatives in medical diagnoses, where critical conditions are missed.

Apart from its limitation on imbalanced datasets, we need to also keep in mind its sensitivity on cost and threshold. In many real-world applications, the cost associated with different types of errors varies. For example, in fraud detection, a false positive (flagging a legitimate transaction as fraudulent) may inconvenience a customer, but a false negative (missing a fraudulent transaction) can be costly. Moreover, accuracy assumes a default threshold for classification (usually 0.5 for binary classification). Accuracy doesn't account for these differing costs. It's crucial to consider the specific context and business goals and complementing accuracy with other evaluation metrics when needed.

Accuracy falls short when dealing with imbalanced data, potentially leading to misleading results when one class dominates the dataset. Accuracy also doesn't consider fairness or the varying impact of different types of errors, treating all mistakes equally. To gain a more complete understanding, it's advisable to complement accuracy with other metrics like precision, recall, F1-score, or area under the ROC curve, especially when involving an imbalanced data

The accuracy metric is best used in classification use cases with balanced data, meaning that the portion of classes is roughly the same. If the data is imbalanced or the number of classes is high, the accuracy score gets increasingly harder to interpret. Here, you should consider using alternative class-wise metrics such as precision, recall, or f1-score metrics. In real-world use cases, this makes sense either way, as you will often have potentially different minimum performance requirements per class. For example, you might want to recognize a person with higher accuracy than a trash can when training a model for autonomous driving.

In classification, accuracy and precision are vital metrics. Accuracy gauges overall correctness considering true positives and true negatives. But in imbalanced datasets, precision is more critical, as it focuses on true positives and false positives. Adjusting the threshold can balance these metrics, aligning the model with your goals.

The interplay between accuracy and other metrics can provide a deeper understanding of a model's performance. I'd like to add that extending accuracy to a class-wise metric can easily offer more nuanced insights, helping to prevent major fallbacks seen in generalized accuracy computations.

Accuracy as a standalone metric may not make lot of sense because its does not give a clear picture of how individual classes are doing wrt to other classes and gives lack of explanation about micro level performance of the model. Along with accuracy we also probably need Precision and Recall of classes of concern to give a holistic view of performance of a typical classification model Also as per me Accuracy is good Macro level metric for performance of the model. But , AUC is a a better Macro level metric for the model . Also I have seen very few instances of accuracy being used as benchmark in solving industry related Classification problems

Combining accuracy with metrics like the Matthews correlation coefficient (MCC) or the area under the precision-recall curve (AUC-PR) provides a better assessment of a model's ability to handle class imbalances and capture its overall predictive power. Incorporating these multi-dimensional perspectives enhances the evaluation process, offering a more accurate depiction of a model's strengths and weaknesses in various real-world scenarios.

Especially in cases where accuracy is not meaningful by itself, e.g., when dealing with imbalanced data or many classes, it should be complemented or replaced by class-wise metrics such as precision, recall, or f1-score. In practice, it can still make sense to optimize for one metric when doing experimentation and set minimum requirements for complementary metrics. For example, you could optimize for accuracy and additionally set minimum requirements for the recall and/or precision of recognizing a human on the street to meet your minimum performance requirements in this especially important class.

Using accuracy alongside other metrics is essential for gaining a comprehensive understanding of a machine learning model's performance, especially in medical image processing. Here's how you can effectively combine accuracy with other metrics: Sensitivity (True Positive Rate): Sensitivity measures the model's ability to correctly identify positive cases (e.g., detecting a disease). It is crucial in medical applications to avoid false negatives. Calculate sensitivity as (True Positives / (True Positives + False Negatives)).

Precision and Recall: Precision measures the proportion of correctly predicted positive instances among all instances predicted as positive, while recall measures the proportion of correctly predicted positive instances among all actual positive instances. F1 Score: The F1 score combines precision and recall into a single metric, providing a balanced measure of a model's performance. Confusion Matrix: A confusion matrix displays the counts of true positives, true negatives, false positives, and false negatives. It helps analyze the types of errors made by the model and provides a broader context for evaluating performance.

In many contemporary workflows, feature engineering and selection, along with the optimization of model architecture and hyperparameters, are often very sophisticated and well-executed. However, the aspect that frequently lags is the quality and depth of the data being utilized. With data enrichment techniques such as embeddings and classifier scores, datasets can be scrutinized for discrepancies using tools like Cleanlab or fastdup. Subsequently, leveraging visualization tools like renumics spotlight for interactive analysis allows for obtaining a comprehensive understanding of data quality. These tools assist in identifying label errors, outliers, and duplicates, and help to improve both training and test datasets' quality.

An error I see often is people using all available features from the data to train models, assuming they will in some way help with the performance. Feature selection and extraction can significantly impact the accuracy of your models. One technique applicable to feature extraction is PCA (Principal component analysis). It reduces the dimensionality of data by transforming the original features into a set of linearly uncorrelated features (principal components) while retaining as much variance as possible. We can use a subset of these principal components to reduce dimensionality while preserving the most important information. PCA is very useful for high-dimensional datasets and can be applied to numerical as well as categorical data.

One of the important capability required to improve accuracy on a continuous basis is to capture the data lineage. It helps to identify potential biases in your data sources and transformations as well as to do root cause analysis when model accuracy issues arise.

considering ensemble techniques like bagging, boosting, or stacking can help enhance accuracy by combining the predictions of multiple models. Regularization techniques such as dropout, L1/L2 regularization, or early stopping can also prevent overfitting, ultimately improving the generalization ability of the model. Additionally, exploring advanced architectures like convolutional neural networks (CNNs) for image data or recurrent neural networks (RNNs) for sequential data can unlock higher accuracy by capturing intricate patterns inherent to those data types, reinforcing the model's capacity to excel in complex tasks.

Improving accuracy in the realm of medical image processing holds profound implications for revolutionizing healthcare diagnostics and treatment. As the integration of artificial intelligence (AI) into medical practices gains momentum, the need to enhance accuracy becomes paramount. This imperative arises from the intricate connection between accurate diagnostic predictions and patient outcomes. The convergence of advanced AI algorithms, medical imaging technologies, and domain expertise has opened new horizons for refining accuracy and optimizing patient care.

Feature engineering: Select and engineer relevant features that capture the important characteristics of the data. This can involve removing irrelevant features, transforming variables, or creating new features. Model selection and tuning: Experiment with different classification algorithms and hyperparameter configurations to identify the best-performing model for your specific task. Data augmentation: Generate additional training instances by applying techniques such as oversampling, undersampling, or synthetic data generation to address class imbalance. Ensemble methods: Combine multiple models, such as through voting or stacking, to leverage the strengths of different algorithms and improve overall accuracy.

Accuracy is good when: - The dataset is balanced with equal distribution of classes - The FPs and FNs are roughly equally weighted and do not vary significantly - Binary Classification : when distinguishing is done between two classes, and no complexities like imbalanced datasets or misclassification costs Accuracy can be Problematic: - Imbalanced Datasets where one class outnumbers the others - Anomaly Detection : where the focus is on rare occurrences, accuracy isn't that informative as evaluation metric - Uncertainty Detection : in situations where model's confidence probability are important - Multi-class : accuracy does not reflect how well model performs for individual classes

As stated previously, Accuracy is not static. If your model got 90% Accuracy on today's data, it might not be 90% accurate on tomorrow's data. The distribution of the data that a model sees in production can change. It is thus important to not only measure metrics while experimenting and building the model but also to do so once the model works on new real-world data. It is useful to then retrain the model on new data.

Accuracy is just one measure we can use to see how good our models are. I would strongly recommend everyone become accustomed to a confusion matrix and the ROC. When dealing with classification problems, you must always select a measure that matches the business case. Meaning you need to know which outcomes matter most to you and then use the measure that is intended to measure that. For example, imagine you are testing a tumor to see if it cancerous. Obviously, you want to maximize identifying it as cancer when it is cancer, and when it isn't, but then you need to think: What's worse, telling some they have cancer when they don't, or telling someone they don't have cancer when they do? Clearly one outcome is way worse then the others.

My view is that defining 'higher-order' KPIs as a means of establishing the link to business performance, and a common understanding of the meaning of accuracy in each context beyond a purist data science interpretation are both crucial steps to creating meaningful and performant ML systems.

Domain-specific metrics: Consider using evaluation metrics specific to your problem domain. For example, sensitivity and specificity are often crucial metrics in medical diagnostics. Validation and test sets: Always evaluate separate validation and test sets to ensure an unbiased assessment of the model's performance on unseen data. Business context: Consider the business implications of model performance. Accuracy alone may not align with the specific goals or requirements of the application. For instance, a false positive in a spam detection system may have different consequences than a false negative. It's important to consider the trade-offs and costs associated with different types of errors.