How can you choose the right outlier detection technique for your machine learning problem?

1 Statistical methods

One of the simplest and most widely used methods for outlier detection is based on statistics. You can use descriptive statistics, such as mean, median, standard deviation, or quartiles, to identify data points that are far away from the central tendency or the normal distribution of the data. For example, you can use the Z-score, which measures how many standard deviations a data point is from the mean, or the interquartile range (IQR), which measures the difference between the 25th and 75th percentiles of the data. These methods are easy to implement and interpret, but they have some limitations. They assume that the data follows a certain distribution, such as normal or Gaussian, and they are sensitive to the presence of outliers themselves, which can skew the statistics and affect the threshold for detecting outliers.

3 Model-based methods

A third method for outlier detection is based on models. You can use machine learning models, such as linear regression, logistic regression, support vector machines (SVM), or neural networks, to fit a model to the data and then use the model's predictions or errors to identify outliers. For example, you can use linear regression to fit a line to the data and then use the residual, which is the difference between the actual and predicted values, to detect outliers. Or you can use a neural network to learn a representation of the data and then use the reconstruction error, which is the difference between the input and output of the network, to detect outliers. These methods are more powerful and sophisticated than statistical or distance-based methods, as they can capture complex and nonlinear patterns in the data and they can handle high-dimensional and heterogeneous data. However, they also have some challenges. They require more data and domain knowledge to train and validate the models, and they can be prone to overfitting or underfitting, which can affect the accuracy and reliability of the outlier detection.

4 Factors to consider

When selecting an outlier detection technique for your machine learning problem, a range of factors should be considered, such as the type and source of outliers, the distribution and dimensionality of the data, the goal and context of the problem, and the resources and constraints of the problem. For instance, you may need to determine if outliers are caused by noise, errors, anomalies, or fraud. Additionally, you may need to consider if the data follows a certain distribution like normal or Gaussian. Furthermore, you must decide whether to remove, keep, or treat the outliers differently. You should also evaluate how much data and time you have available and how complex and accurate your outlier detection technique needs to be. By taking all these factors into account, you can choose a suitable outlier detection technique that will improve your results.

5 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?