How can you remove outliers for a specific ML task?

Outliers should not really be removed, In reality they are legitimate data points that are part of actual data. Nonetheless there are two scenarios 1) data collected might be faulty and there might be some wrong responses which should be removed through proper investigation. 2) Data points are too few to capture actual data distribution which creates an illusion of outliers. Given enough data these outliers will be part of the distribution. generally in second case outliers are removed due to the incapability of model rather than actual usefulness of data In summary better data collection and increasingly capable models will solve the problem of outliers without actually discarding them

Removing outliers is crucial because they have the potential to skew the results of your model and lead to poor generalization. However, the definition of outliers may vary depending on the specific case and domain 1 Begin by visualizing your data using various plots such as boxplots e.t.c 2 Calculate statistical measures such as the mean, s.d, and quartiles. Utilize these measures to establish a threshold for identifying outliers 3 There are several methods to handle outliers, and the choice should be based on the nature of your data. Domain knowledge is key in this process, and the interquartile range can be useful 4 Once you have removed the outliers, it is important to perform EDA to ensure that the distribution appears more reasonable

In my experience, in addition to the mentioned methods for identifying outliers, data clustering can be very useful in this regard. Clustering-based outlier detection leverages the inherent structure and patterns within the data to detect outliers. By grouping data points into clusters, it identifies points that are assigned to small clusters. Various techniques, such as density-based clustering and outlier scoring methods like LOF and Isolation Forest, aid in quantifying the outliers. This approach finds applications in diverse domains, including fraud detection, anomaly detection, and quality control, allowing for the identification of data points that deviate significantly from the distribution of the majority of the dataset.

Removing outliers for a specific ML task can be done by – Identifying outliers using statistical methods like Z-scores or the Interquartile Range (IQR) Visualising data with boxplots or scatter plots to spot anomalies Filtering out any data points that fall beyond an acceptable range based on domain knowledge Applying robust scaling techniques that reduce the influence of outliers on the model

When removing outliers from your dataset, it's essential to consider the trade-offs and consequences. While eliminating outliers can reduce noise and distortion, it may also result in the loss of valuable information or insights hidden within them. Additionally, it can reduce data variability and diversity, impacting the generalization and robustness of machine learning models. Removing outliers may introduce bias or distortion if they are systematic or correlated with other variables. To make an informed decision, always assess the validity and relevance of outliers and compare model results with and without them.

Removing outliers must be done after carefully considering the tradeoffs as Sanjay has correctly posited, and If after this considerations you do decide to remove the outliers, it's crucial to document and justify your decision. This helps in maintaining transparency and ensures that others understand your data preprocessing steps.

Replacing outliers effectively demands a blend of statistical techniques and domain expertise. A common approach is winsorization, capping extreme values to a specified percentile, thus reducing variance without losing data. Another technique is imputation, substituting outliers with mean, median, or mode, which works well for mild anomalies but could introduce bias if overused. Advanced methods like k-nearest neighbors (KNN) or regression imputation leverage patterns within the data for a more informed substitution. Whichever method you choose, it should align with the data distribution and the predictive power of your ML model to ensure data fidelity and model accuracy.

Mean/Median Imputation: Replace outliers with the mean or median of the feature. This is a straightforward approach and is effective when the data follows a normal distribution. Custom Value Imputation: Replace outliers with a predefined constant or custom value. This can be useful when you have domain knowledge that suggests a specific value for outliers. e.g. replacing outliers in a survey dataset with a fixed value might be appropriate if outliers represent data entry errors. Percentile Imputation: Replace outliers with values at specific percentiles of the data distribution. E.g. you can replace values above the 99th percentile with the value at the 99th percentile.

Replacing outliers is an enticing proposition, but it should be done carefully. You might end up "embedding" patterns (over-representing data) in your sample which can lead to model bias. Understanding your imputation method, and balancing between complexity and simplicity in your imputation logic is paramount.

Scaling outliers requires delicate handling to retain data integrity. Techniques like robust scaling, where the median and interquartile range establish the scale, diminish the impact of outliers on your model's performance. This non-parametric method ensures that extreme values do not distort the overall data distribution, thus preserving essential structures. When scaling, it's critical to understand the underlying distribution and variance, as well as the model's sensitivity to these extremes Choose your scaling method wisely, as it can significantly influence your ML model's ability to generalize and perform under varying data conditions.

Winsorization: Winsorization limits the values of outliers by replacing them with the nearest non-outlier data point. You can choose to replace outliers with values at a specific percentile (e.g., 99th percentile) to constrain their impact. This technique effectively trims extreme values without removing them from the dataset. Log Transformation: Applying a logarithmic transformation is useful for data that exhibits exponential or highly skewed distributions. It compresses the range of values and reduces the influence of extreme values while preserving their presence in the dataset. The degree of skewness will determine the base of the logarithm (common choices include base 10 or the natural logarithm, base e).

Scaling outliers can be a pragmatic approach when dealing with extreme values. Instead of outright removing them, this method can help temper their impact on your machine learning models while retaining some useful information. It's worth noting, however, that scaling outliers is not a one-size-fits-all solution. The technique you choose, whether it's logarithmic transformation, standardization or another method, should align with your data's characteristics and distribution.

Begin by visualizing the data using appropriate plots such as scatter plots, box plots, histograms, or Q-Q plots. Visualization allows you to identify potential outliers and gain an initial understanding of their impact. Utilize statistical techniques to identify outliers. Common methods include: Z-Score: Calculate the z-score for each data point and consider those with z-scores above a certain threshold as outliers. IQR (Interquartile Range): Identify data points that fall outside the bounds of Q1 - 1.5 * IQR and Q3 + 1.5 * IQR as outliers. Leverage domain knowledge to evaluate whether identified outliers are valid or erroneous. Some data points that appear as outliers might be genuine and important in the context of the problem.

I'd argue that evaluating your outliers is the first step. Sometimes removing outliers is the best solution and other times it's not. What's most important is to know your data. if you are using supervised learning and notice inconsistencies in e.g. detecting where the front door of a car is, then the problem may be found in your annotations. If you are using clustering and find outliers that may mean you have an underrepresented group of data. So know your data well before discarding anything. That makes your results more reliable and less biased too.

Importante documentar e justificar por que os outliers foram removidos ou substituídos, para a transparência do processo. Muitas vezes é preferível considerar algoritmos robustos que não sejam sensíveis a outliers, em vez de removê-los.

But how do we know outliers grow to different categories along the way to the society and culture of our mother Earth for the good 😊

PyOD (Python Outlier Detection) is a specialized library for outlier detection. It provides a wide range of algorithms and methods for identifying outliers in your data. You can also explore Z-Score and IQR Functions. scipy.stats library for calculating Z-scores and Interquartile Range (IQR) to identify and remove outliers based on statistical methods.