Contents

- 1 How do you find outliers in categorical data?
- 2 How do you find anomalies in data?
- 3 How do you identify anomaly?
- 4 How do you identify anomalies in time series data?
- 5 Does isolation forest work with categorical data?
- 6 What is the difference between outliers and anomalies?
- 7 How do anomalies affect data?
- 8 How do you deal with anomalies in time series data?
- 9 What can you do with anomalies in data?
- 10 Is Isolation Forest supervised or unsupervised?

## How do you find outliers in categorical data?

As per my understanding, there is no concept of outliers detection in categorical variables(nominal), as each value is count as labels. Based on frequency(Mode), we can’t do outliers treatment for categorical variables.

## How do you find anomalies in data?

The simplest approach to identifying irregularities in data is to flag the data points that deviate from common statistical properties of a distribution, including mean, median, mode, and quantiles. Let’s say the definition of an anomalous data point is one that deviates by a certain standard deviation from the mean.

## How do you identify anomaly?

Arbitrarily set outliers fraction as 1% based on trial and best guess. Fit the data to the CBLOF model and predict the results. Use threshold value to consider a data point is inlier or outlier. Use decision function to calculate the anomaly score for every point.

## How do you identify anomalies in time series data?

The entire process of Anomaly Detection for a time-series takes place across 3 steps:

- Decompose the time-series into the underlying variables; Trend, Seasonality, Residue.
- Create upper and lower thresholds with some threshold value.
- Identify the data points which are outside the thresholds as anomalies.

## Does isolation forest work with categorical data?

Isolation Forest has been adapted to categorical data in [3], where the authors used one-hot coding, but this extension artificially increases the importance of such features, making it unsuitable in practice. If the feature is categorical, a split value is chosen at random among possible values.

## What is the difference between outliers and anomalies?

Outlier = legitimate data point that’s far away from the mean or median in a distribution. While anomaly is a generally accepted term, other synonyms, such as outliers are often used in different application domains. In particular, anomalies and outliers are often used interchangeably.

## How do anomalies affect data?

It is an anomaly. Taking many repeat measurements or having a large sample size to analyse will improve accuracy. Anomalous results can be easily spotted in the data and discarded, leading to a more accurate calculation of the mean.

## How do you deal with anomalies in time series data?

From a very high level and in a very generic way, time series anomaly detection can be done by three main ways:

- By Predictive Confidence Level Approach.
- Statistical Profiling Approach.
- Clustering Based Unsupervised Approach.

## What can you do with anomalies in data?

5 ways to deal with outliers in data

- Set up a filter in your testing tool. Even though this has a little cost, filtering out outliers is worth it.
- Remove or change outliers during post-test analysis.
- Change the value of outliers.
- Consider the underlying distribution.
- Consider the value of mild outliers.

## Is Isolation Forest supervised or unsupervised?

It is a tree-based algorithm, built around the theory of decision trees and random forests. When presented with a dataset, the algorithm splits the data into two parts based on a random threshold value. It is important to mention that Isolation Forest is an unsupervised machine learning algorithm.