Questions tagged [outlier]

For questions regarding outliers or unusual points in the data.

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4answers
75 views

Machine Learning methods for finding outliers

I have a csv files of thousands of lines, the data is put down into a Dataframe of columns. Some of the column have text information while others might have numbers. I want to detect anomalies or ...
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1answer
40 views

For outliers treatment, clipping, winsorizing or removing?

I came across two different techniques for treating outliers winsorization, clipping and removing: Winsorizing: Consider the data set consisting of: {92, 19, 101, 58, 1053, 91, 26, 78, 10, 13, −40, ...
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1answer
16 views

looking for approaches to detecting outliers in individuals unequal sequential time series

I am looking for approaches related to outlier detection in time series. Example: A person visits hospital overtime on multiple bases and there are some measurements made (bmi, blood_pressure, ...
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3answers
62 views

Use of Standardizer to handle outliers?

I have a dataset with 60 columns and 5K records. There are few columns which has outliers. I understand that there are multiple approach to handle outliers. Actually I don't wish to drop the data as ...
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1answer
17 views

Are cluster feature and micro-cluster good summury statics for outlier detection in high dimensional data streams?

I'm dealing with outlier detection in data streams. I'm looking for a way to summarize my data and obtain important statistics such as means and variance, etc. I want to know if the cluster features ...
2
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1answer
29 views

Remove noise by clustering on which step of pre-processing is better?

I am working on a classification task. The dataset is a UCI data set about machine learning with 200 observations and 2 classes. Part of my model includes the following preprocessing steps: remove ...
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2answers
86 views

how to handle outliers for clustering algorithms?

I am wondering what's the best way to handle outliers when using non-supervised clustering algorithms? Thanks!
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0answers
12 views

Creating Flags Instead of Designated Values

I'm working with http://archive.ics.uci.edu/ml/datasets/Bank+Marketing# dataset in order to create a model. We're going to use it in a presentation to introduce people our new data science environment....
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1answer
32 views

What method is recommended after outliers removal?

I have a data of mice reaction times. In every session, there are some trials in which the mouse "decides of a break" and responds after a long time to these specific trials. I was thinking of ...
0
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1answer
68 views

Why labels are not used in outlier detection algorithms?

I read this article from sklearn: https://scikit-learn.org/stable/modules/outlier_detection.html While these algorithms are very useful for outlier detection, I'm surprised to see that they are not ...
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0answers
7 views

Perform unsupervised anomaly identification with causation in Python?

I have a time series data, which contains information from various sensors measured at every 20 minutes interval. I would like to use information from all these sensors as features to a Deep Learning/...
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1answer
93 views

Explainable anomaly detection

There are plenty of working for explaining prediction in supervised learning (e.g. SHAP values, LIME). What about for anomaly detection in unsupervised learning? Is there any model for which there ...
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1answer
22 views

Functions in scikit that detect outliers automatically?

I know a way to visualize outliers is to make a box plot, but wanted to know if scikit had any quick ways to detect outliers for each variable?
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2answers
552 views

How can I replace outliers with maximum non-outlier value?

I am doing univariate outlier detection in python. When I detect outliers for a variable, I know that the value should be whatever the highest non-outlier value is (i.e., the max if there were no ...
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0answers
43 views

dataset shift, covariate shift or sample selection bias in small subsets?

I try to show from which training data size on which machine learning method (CNN, SVM etc.) achieves better performance. For this I would like to use subsets of different sizes from the datasets of ...
1
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2answers
43 views

Standard deviation as outlier detection

I have a quite basic question: A standard deviation is defined such that around ~66 % of the data lies within it. And around ~99 % within three standard deviations. When I wanna' use the standard ...
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2answers
52 views

How to improve identification of outliers for removal

I have many datasets where the measured value is either "normal" (i.e. the process is running" or abnormal (i.e. process is not running). Unfortunately, I don't have a measurement that clearly ...
0
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1answer
29 views

How to convert a non gaussian distribution into a gaussian destribution?

Suppose I have a dataset inwhich there are few dimensions that distribution over them is non gaussian and this means, skewness is nonzero (possitive or negative). This is caused by some outliers in my ...
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0answers
19 views

Machine Learning alternative for hashing

Is there a Machine Learning technique that can used to detect the slightest change in data? I know this can be done using a hash but I was just wondering if there is any machine learning technique out ...
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1answer
32 views

Which is the correct method for outlier analysis on a dataset for modelling?

I'm trying to build a regression model but my data-set have many outliers points which I need to analyze and then remove them. There are two ways to do it, 1) First do all the analysis on every ...
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1answer
149 views

Isolation forest - grouped by

I'm trying to use isolation forest algorithm for outliers detection. Data has 2 columns: id and REV. Below code gives me ...
5
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1answer
536 views

In elbow curve how to find the point from where the curve starts to rise?

I am computing a distance metric on my data. The result is then being sorted in ascending order. The samples having distance more than a specific threshold are to be marked as outliers and will be ...
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1answer
112 views

Dealing with new outliers after capping

I'm trying to cap outliers in a column of my pandas DataFrame. Here's the boxplot for a column of my original data. So, using code from this stackoverflow answer, I tried capping outliers. Here's ...
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0answers
24 views

How can detect and highlight outliers by using gaussian function and normalize the data elegantly?

I tried to normalize the data by using Gaussian function 2 times on both positive and negative numbers of each parameter of this dataset. The dataset includes missing data as well. The problem is I ...
2
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0answers
52 views

unsupervised outliers detection - possible solution?

I have dataset of traders' transaction data: trade id, date, stock id, sector of stock id, buy-or-sell, volume $ The goal is to identify anomalies in transactions data of traders. For example to ...
5
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1answer
695 views

Isolation forest sklearn contamination param

I'm working on an unsupervised anomaly detection task on time series using isolation forest algorithm. I'm developing in Python, more in detail using sklearn. I found out a lot of examples on this, ...
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1answer
25 views

What is meant by outliers in text data set. How to detect them?

I know that outliers are present in data but their behaviour varies a lot from remaining data points. But today while learning about naive-Bayes they mentioned that naive-Bayes can affected by the ...
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1answer
2k views

How to remove outliers using box-plot?

I have data of a metric grouped date wise. I have plotted the data, now, how do I remove the values outside the range of the boxplot (outliers)? All the ['AVG'] data is in a single column, I need it ...
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3answers
52 views

good algorithm for outliers detection

I have 2 independent data sets (1. 300 rows and 2.3000 rows) with 6 months trades observations for 50 traders. In both datasets I have: trader id, stock title, buy/sell volume, date of trade, sector ...
2
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1answer
110 views

Number of Nodes in Isolation Forest

I am currently reading this paper on Isolation Forest. At page 3, there is a definition of Isolation Tree and there are a couple of sentences that I don't understand: Given a sample of data X = {...
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5answers
753 views

Time Series:Outlier Detection

I have time series data which looks like the graph mentioned below. I am familiar with the method of removing outliers based on the standard deviation and median values. Drawback of these methods are ...
2
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5answers
203 views

Methods to detect this kind of outliers

Background I don't know much about (or to say anything) about data science or machine learning. But I'm interested in learning and thought this problem can be solved with machine learning. That's why ...
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2answers
128 views

How to scale outputs from AutoEncoder from multiple models?

I have a problem for which I have not been able to find any answers in my search so far. BACKGROUND I am working on an anomaly detection problem on machines utilising an auto-encoder. I am building ...
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1answer
30 views

How to distinguish between normal fluctuation and outliers in ARIMA model?

I have a dataset about sales per day of certain products at the ITEM/DAY/STORE level , I've plotted the series and visually examined it for any outliers, volatility, or irregularities. And this is ...
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1answer
30 views

Clustering unbalanced dataset

The data I am working on has some really large price values and some really small values. What I did was first perform feature bagging on the data and got them labelled to (0,1) and then did ...
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2answers
74 views

Outlier detection for Disk Space Usage

I would like to do outlier or anomaly detection on the disk free space data. Sample dataset as below (I don't have any label dataset): ...
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2answers
66 views

A Proper Outlier Detection For the Attached Figure

I am wondering how I could detect the red points as outliers using an algorithm (best method for this scenario) not through visualization since it is clear that they are outliers in the figure. ...
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0answers
35 views

How to find anomalies/outliers in Panel Data?

I have panel data based on 900000 different entities with 384 time steps and the data is not normally distributed. I am looking for outliers/anomalies, this is unsupervised as I have no examples of ...
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0answers
17 views

Why is my LOF algorithm producing the opposite result it should?

What could cause the local outlier factor (LOF) to output below 1.0 for outliers and above 1.0 for inliers? I have my code sort of working just by inverting the output, but I can't figure out what's ...
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0answers
67 views

Isolation Forest

Can some one please explain Isolation Forests more clearly? Everywhere I search, I find the same explanation: Isolation Forest ‘isolates’ observations by randomly selecting a feature and then ...
2
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0answers
44 views

Replacing mean by median over batch-size to lessen the impact of outliers

In the case of training a Neural Network on a regression task. Assuming the data has a significant amount of outliers. Provided that the error needs to be RMS and not MAE. Can it be better (as in less ...
0
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1answer
110 views

Algorithm suggestion for anomaly detection in multivariate time series data

I have time series data containing user actions at certain time intervals eg ...
2
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2answers
84 views

How to visualize change in a distribution with a few outliers that account for a very large percent of the total?

I'm working on an edtech product where some of our traffic lands on webpages about textbooks. Textbooks belong to subjects like Algebra, Calculus and Spanish. In each of our subjects, we have "...
7
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1answer
128 views

How to decide how many n_neighbors to consider while implementing LocalOutlierFactor?

I have a data set with rows: 134000 and columns: 200. I am trying to identify the outliers in data set using LocalOutlierFactor from scikit-learn. Although I ...
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0answers
47 views

What’s more appropriate for outlier detection? Classification or Regression?

I'm using PyTorch and i’m working on a data set where I only care about “outliers” from the norm. The data is comprised of long time series with gaussian distribution, while once in a while there is a ...
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2answers
167 views

which outlier detection technique?

I'm new to data science. I have a question on anomaly detection techniques. There are several anomaly detection techniques such as statistical, density based, depth based, clustering, etc.. Given a ...
2
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1answer
52 views

Gracefully removing observations with outliers in N fields

I have a function. ...
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1answer
116 views

Is there any formal explanation for the sensitivity of AdaBoost to outliers?

AdaBoost is known to be sensitive to outliers & noise. However, the explanation seems to be hard to found or nontrivial.
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2answers
515 views

Effect of outliers on Naive Bayes

Are Naive Bayes algorithms affected by outliers in the data? Suppose there is a data set, does one need to remove outliers before applying Naive Bayes?
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3answers
935 views

Why is Local Outlier Factor classified as Unsupervised if it requires training data with no outliers?

In Scikit-Learn, the Local Outlier Factor (LOF) algorithm is defined as an unsupervised anomaly detection method. So then I don't understand why this algorithm requires pre-filtered training data. ...