How can you determine the threshold for outlier detection?

In finance, a sudden stock price spike caused by breaking news might create outliers in a dataset. These anomalies could stem from legitimate market shifts or data entry errors. Analysts must set a threshold to distinguish valuable information from outliers, deciding whether to retain, eliminate, or adjust the data based on their impact on statistical properties or assumptions.

Outlier detection is essential for data reliability, preventing fraud, and strategic decision-making. It safeguards against errors, fostering precision in analytics. Beyond data integrity, it guides risk management by identifying potential threats and opportunities. Automated algorithms streamline the process in the era of big data, enabling proactive responses to outliers. Ultimately, this proactive approach transforms data into a strategic asset, enhancing organizational agility and resilience in the face of rapid change.

Detecting outliers is an essential step before training an ML model. There are various reasons for this, such as: 1. Outliers may indicate errors in data collection, entry, or processing. 2. Outliers can significantly affect the results of statistical analyses. Measures such as the mean and standard deviation are sensitive to extreme values. 3. Outliers can have a significant impact on machine learning models. They can distort the training process, leading to inaccurate models. 4. Outliers can represent anomalies or unusual patterns in the data. Detecting these anomalies is crucial in various applications, such as fraud detection in financial transactions etc.

Detecting outliers is vital for data analysis. It ensures data quality by spotting errors or anomalies like inaccuracies or entry mistakes. Addressing outliers preserves statistical assumptions, maintaining analysis integrity. Managing outliers improves machine learning model performance, enhancing predictive accuracy. Some outliers reveal meaningful patterns or rare events, providing valuable insights. Handling outliers improves the interpretability of summary statistics and visualizations. It is a crucial step in data cleaning, ensuring subsequent analyses are based on reliable data. In summary, outlier detection is essential for data quality, statistical validity, and overall reliability in analyses and models.

Determine the cause first and take action on it next. A faulty sensor giving you -200 degree reading is a different outlier than finding Elon Musk's income in dataset of US household annual incomes. Then we can have strategies for imputation or outright rejecting the outlier.

Imagine a photography contest where judges are training a new assistant to pick winning photos. The contest receives a vast variety of photos, but some are very unusual - a blurry landscape, a too-dark portrait, or a strangely angled street view. These odd photos are like outliers in an image dataset. If the assistant is trained considering these unusual images as examples of good photography, he might end up selecting poor-quality photos for future contests. Just like in the contest, in machine learning, it's important to spot and understand these 'unusual photos' in image datasets. Detecting outliers is about teaching the AI to recognize and learn from the right examples, ensuring it performs well in real-world scenarios.

Just remember there is no one-size-fits-all approach. Even the word "threshold" implies you already know a single value that you are measuring (or something equivalent). Suppose you are working with 400x400 pixel images. Where is the "single value" on which you can apply a threshold? Your image dataset is almost t certainly not distributed normally in R^n (where n is the number of pixels in the image), so standard deviation in image space will not make sense. (If you have reason to believe your data is ~gaussian, look up Mahalanobis distance to read about distance w.r.t. non-spherical high-dimensional gausians). If thresholding will be the answer, then the key question is probably figuring out what value to threshold, not by how much.

There is no global answer to this question, this totally depends on the dataset and problem statement. For instances, sometimes domain knowledge can be used to identify the outliers. Sometime you can use visualization methods and statistical methods get insights of the data. These methods can be used to identify threshold for outliers. Whenever choosing any threshold, make sure you don't loose any sensitive data, that can affect the model accuracy in a drastic way.

That's a crucial step needed to be considered in any data. General tendency is not to go with mathematical comparative, instead try out the best threshold after Visualization of data. It's actually hard to stick on any particular mathematical inequalities.

Para elegir un umbral adecuado para la detección de valores atípicos en IA necesitamos combinar estadística y entendimiento profundo del contexto de los datos. Un umbral demasiado restrictivo puede llevar a la eliminación de datos valiosos, mientras que uno demasiado laxo puede fallar en identificar anomalías importantes. Existen métodos estadísticos (desviación estándar o el rango intercuartílico) que proporcionan un punto de partida. Es esencial considerar la naturaleza de los datos y el objetivo específico del análisis. La colaboración con expertos en el dominio de los datos es clave para definir umbrales que reflejen tanto el conocimiento técnico como el contexto aplicado.

The choice of threshold depends on the nature of your data and the specific application. For normally distributed data, a common approach is to use a standard deviation method, where points lying more than 2 or 3 standard deviations from the mean are considered outliers. For skewed distributions, percentiles can be effective.

Set the decision boundary by adjusting the threshold based on predicted probabilities. Begin with a default (e.g., 0.5) and evaluate metrics like precision and recall. Analyze trade-offs and tweak the threshold accordingly. Explore ROC curves for sensitivity-specificity balance or precision-recall curves for imbalanced datasets. Incorporate domain knowledge, experiment iteratively on validation data, and consider dynamic thresholds for adaptability. Implement the chosen threshold in code, validate on new data, and monitor regularly. Document decisions for transparency, aligning model output with goals in a concise and thoughtful process.

In Python, to implement a threshold for outlier detection, first calculate the interquartile range (IQR) by subtracting the 25th percentile from the 75th percentile of the data. Next, establish the lower and upper bounds by extending 1.5 times the IQR below the 25th percentile and above the 75th percentile, respectively. Then, create a filtered dataset by retaining only those data points that fall within these bounds. This results in a dataset purged of outliers, ready for further analysis or visualization.

Implementing a threshold involves applying statistical techniques such as z-scores, interquartile range (IQR), or leveraging machine learning algorithms. The chosen method should align with the data distribution and characteristics.

1.Select a method that aligns well with individual data distribution and goal. 2. Using selected method, calculate relevant statistics like Mean, Standard deviation, Q1,Q2, etc. 3. Define threshold based on chosen method's guidelines like 2-4 standard deviation from the mean. 4. Implement the code in preferred language.

Once you apply the threshold on the data, get a quick look at the data ??. How well has the threshold worked? Does the data follow the same assumption that you had previously? Does the remaining data holds the same stats as you wanted? Has all the outliers eliminated?? or has been taken care of? Check the before & after thresholding of the data visualizations ??. Use the statistical methods for the verification. Methods like mean, median, standard deviation, skewness etc. can be useful. Once you are satisfied with all the answers You can check the results lf ML model and verify the matrics before applying the threshold and after applying threshold.

You can evaluate the efectivess of your outlier removel by using simple statistics metrics, such as, average, variance and standard deviation. Data visualization may also be helpfull.

Another helpful way apart from visualization is by splitting the data into train and validation sets: - Try different thresholds on the training set - Evaluate the performance of the thresholds on validation sets using metrics like accuracy, precision/recall, etc and pick the threshold giving the best performance

Evaluate a threshold by starting with a default (e.g., 0.5) and measuring metrics like precision, recall, or ROC-AUC. Analyze ROC and precision-recall curves to understand trade-offs. Choose metrics aligned with objectives—precision for fewer false positives, recall for fewer false negatives. Iteratively adjust the threshold and experiment on validation data, visualizing changes. Incorporate domain knowledge and consider practical implications. Explore dynamic thresholds for adaptability and ensemble-specific techniques. Cross-validation ensures robustness, and documentation aids transparency and reproducibility. This dynamic process optimizes model performance based on specific goals and evolving requirements.

To evaluate an outlier detection threshold, visualize the data before and after applying the threshold using plots to check for changes in distribution. Analyze statistical changes in mean, median, and standard deviation to understand the threshold's impact. Assess your machine learning model's performance metrics with and without the outliers to gauge improvement. Lastly, validate your findings with cross-validation and domain expertise to ensure generalizability and relevance.

To improve an outlier detection threshold, make adjustments based on data distribution, experiment with different threshold values, and validate the results with expert feedback. This iterative and adaptive process ensures that the threshold is effective and relevant for the specific data set.

Improving the limit of your threshold may be a trick thing. Some things you may test are: - Looping your code trough an interval of values of threshold and see wheter your model improves or not. - Talking to business stakeholders to understand if they have a known profile for your case (customers, sales, etc)

To improve a threshold, start by evaluating current model performance and analyzing ROC and precision-recall curves. Clearly define objectives—minimizing false positives, false negatives, or finding a balance. Choose an aligned metric like precision, recall, or F1-score. Iteratively adjust the threshold, testing on validation data for generalization. Explore dynamic thresholds for adaptability, use ensemble-specific techniques for ensemble models, and employ optimization algorithms or grid search. Incorporate domain knowledge and consider business goals. Cross-validation ensures generalization, and continuous monitoring with documentation supports sustained improvement. Treat threshold optimization as an iterative, dynamic process.

To enhance your outlier detection threshold, consider experimenting with various statistical methods like z-scores and percentiles to see which aligns best with your data. Additionally, combining methods, such as applying IQR followed by z-score analysis, can offer more precise results. Incorporating domain knowledge and expert opinions can also provide contextually relevant adjustments to your threshold. For a more sophisticated approach, explore machine learning techniques like clustering or isolation forests, ensuring a balance between accuracy and the preservation of valuable data patterns.

Thresholds can be evaluated using metrics like precision, recall, F1-score, or area under the Receiver Operating Characteristic (ROC) curve. The evaluation should consider the impact of false positives and false negatives on the overall model performance. 5. How to improve a threshold? Continuous monitoring and adjustment of the threshold may be necessary as data patterns evolve. Fine-tuning based on feedback from model performance or domain-specific changes can enhance the effectiveness of outlier detection.

Deciding wheather an outlier is going to cause problems or it is valuable information introduced to data from changes in business decision can be an important information. Abrupt changes in data introduced by uncommon business process distortions can be considered valuable information to determine the future potential after this outlierish change happened. These information can be checked with business experts or by expanding the dataset and observing additional resulting changes.

Consider the interpretability of results, scalability of the chosen method, and the potential impact of outliers on downstream tasks. Collaborating with the AI and LinkedIn community can provide diverse perspectives and insights for refining outlier detection strategies.

Determining a threshold can be a challenging process. The true distribution is always unknown to us, so neither visualization nor a validation set can guarantee an optimal threshold. It is a trial-and-error process that requires online learning and an adaptive strategy when applying high-scale production. One takeaway is to always monitor the threshold and its performance, as distribution drift is inevitable.

This indirectly relates to a research paper I presented at an IEEE Conference in 2018. The paper I worked on was about data fusion to ensure data integrity for mobile sensors. Detecting whether or not a data value was a valid data point is a direct corollary to whether or not it is an outlier. The algorithm I used was a form of multidimensional expectation maximization algorithm. I took values along both time and space dimensions as well as known correlations between different measurements to see if a value was "valid". In the case of outliers, you can grab different subsets of the data and do the same thing to determine whether or not a datapoint may be an outlier.

Various outlier detection techniques exist, broadly categorized in classes. (1) Statistical based techniques (2) Distance based techniques (3) Density-based techniques (4) Clustering based techniques (5) Frequent pattern mining based techniques (6) Classification based techniques (7) Segregation based techniques (8) Information theory based techniques (9) Quality based techniques In frequent pattern mining, identify simultaneous patterns with support exceeding a threshold. Normal behavior exhibits these patterns, while outliers deviate, often seen in shorter or less frequent patterns. Reference: Souiden, Imen et al. "A survey of outlier detection in high dimensional data streams." Computer Science Review, 2022