Questions tagged [outlier]

For questions regarding outliers or unusual points in the data.

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can we use tanh activation function to detect outliers?

Can we use tanh activation function to detect outliers ? Does my image below true for dataset outliers (after training model with tanh activation function) ?
user3668129's user avatar
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Outlier detection with elliptic envelope - unexpected error

I am trying to detect outliers with sklearn.covariance.EllipticEnvelope for a single variable, but it throws an unexpected error. Here is an example the reproduces ...
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Outlier Detection for non linear data for regression

I'm grappling with two parameters exhibiting an 82% correlation, showcasing a non-linear relationship and continuous fluctuations. Despite attempting LocalOutlierFactor in scikit-learn, outlier ...
RAJESH KOYI's user avatar
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Min-Max Scaling more sensitive to outliers than 'Simple Feature Scaling'?

I am confused as to the pros and cons of two different approaches to normalization: Min-Max Scaling, and what the lecturer in the course I am taking refers to as 'Simple Feature Scaling'. The latter ...
Chris Bedford's user avatar
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Outlier Handing when most value is 0

Just a question, i know that when we plot against the distribution of numerical data, those who fall outside of the boxplot (diamond shape point) are considered outlier. However, i met a case where ...
Razark's user avatar
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Univariate anomaly / outlier detection

I'm facing a problem that seems 'easy,' but I've been struggling with it for a while now in the field of anomaly/outlier detection. I have a dataset of around 60K data points. Each data point is part ...
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How to deal with a small extra cluster in a tabular data?

I am working on a high dimensional tabular dataset with 1600 features and 9440 rows. No matter how I select the features, when I try to project my data into a 2d or 3d graph using dimensionality ...
Tanmay Sharma's user avatar
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Outlier filtering from time series data

I have time series data that I eventually want to cluster after using dimensionality reduction. I am thinking about how to handle outliers. The data has seasonal/periodic patterns. I have tried IQR ...
Jim A's user avatar
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Do outlier data in target variable cause a problem for GBM's?

I wonder if I should exclude outlier (legit data, not wrong readings) data from my dataset using gradient boosting. Let's say we try to predict water damage for regular houses and 99% of data is in 0-...
morqueatsz's user avatar
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Using nearest neighbor in RANSAC

I found many resources online talking about nearest neighbor concept in RANSAC. For example, figure 2 of this paper, this article and this repo talk about nearest neighbor in the context of RANSAC. ...
RajS's user avatar
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Bivariate Outlier Detection

I'm trying to find outliers in a bivariate data set. One of the input variables is the amount of time that it takes a user to do a task in our system in seconds (rounded to integer). Most interactions ...
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Binary classification using xgboost

Why when adding new features in my ADS for a binary classification using XGBOOST my score and uplift has decreased ? What is the best way to treate categorical features or other features in order that ...
Warda_IDRIS's user avatar
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Remove outlier from image array in Python

Could someone please suggest me what would be the best way to remove such huge number of outlier data from the image. The regular clipping between data range in numpy array would simply reduce the ...
hillsonghimire's user avatar
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How would normalizing be affected by outliers? And how to avoid it?

I have a data set that boils down to Three clomuns: 1.Supplier name 2. Number of transactions with supplier 3. Total value of those transaction. I'm trying to find the best way to rank all suppliers ...
Rakuzan's user avatar
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2 answers
2k views

ML regression for skewed data

I'm trying to build a simple regression model to start with but my Y variable is very skewed to the right. My Y represents the number of views per day for a webpage and all the values are above 0. I ...
Karolina's user avatar
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Finding outlier for millions of data that can be used cluster-based algorithm

I have millions of data that each have many features. But as long as the value of a feature is not in the acceptable range, that data will be considered an outlier. And I need to find the acceptable ...
Geoffrey's user avatar
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Outliers: How to handle data values of future

I am learning ML. I have a dataframe with some features and a target column. For simplicity condiser X has one feature, for eg X ...
winter's user avatar
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How to apply a tree-based model with numerical and categorical values to find outliers

I have a dataset which has a column of prices, a column of dates, and various other columns of numerical and categorical values. I would like to find outlier prices based on all the columns in the ...
ImNotSureAboutStats's user avatar
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Dealing with outliers

I'm doing some data analysis on the UCI "Adult Dataset". I have a numerical feature called "hours-per-week" and another feature called "age". These are the only numerical ...
Beatriz Gonçalves's user avatar
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Testing RANSAC regression model

I am going to build the model (e.g. multiple linear regression) to predict the appartment cost in my city. First I have to find outliers in training data. For this task RANSAC regression algorithm ...
Irina Svist's user avatar
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1 answer
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Python Library Trend time series multivariate

Our csv contains 36 columns 1 date time column collected every 30 mins 3 variables (count,latency,Totaltime) x 10 Features(user io, serverio ,concurrency ..etc ) Of different data points from the ...
trent's user avatar
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Can/should a logical multi-class classification anomaly detection system be described as "unsupervised machine learning"?

I would like to ensure that my use of terminology is accurate. My question is: what terminology should I be using in this case? The system I am building assigns classes (-1, 0, +1) to observations ...
HackJob99's user avatar
6 votes
2 answers
2k views

Remove or not to remove outliers

Are there any known academic sources that point towards supporting not removing outliers? Let say if the outlier is a natural occurrence or it has relationship to the value of target variable
Kusisi Karem's user avatar
2 votes
1 answer
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Can GridSearchCV be used for unsupervised learning?

im trying to build an outlier detector to find outliers in test data. That data varies a bit (more test channels, longer/shorter testing). First im applying the train test split because i want to use ...
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what is the difference between 'object detection' and 'outlier detection' in computer vision?

If I'm looking at drone footage, and I'm looking for tennis courts, then I'm doing object detection. If I decide that a tennis court is an 'outlier' as opposed to the rest of the landscape, am I now ...
tumultous_rooster's user avatar
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Find the most impactfuls parameters multivariate output unsupervised ML

I am currently on a proect where my df has more than 600 parameters of analog sensors (A parameters) and about 50 other parameters (F parameters). I want to find for each of these 50 parameters (F ...
Art's user avatar
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How much percentage of outliers are allowed in a data

I am buliding a machine learning model with logistic regression. I am dealing with blood transfusion data set. In which there is a feature,Total_volume, I found that there is more than 5% of outliers ...
Neenu Prasad's user avatar
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Anomaly prediction/forecasting in timeseries?

What options exist in order to forecast when next observation will be an outlier in a time series? Initially, I thought to train a simple forecasting model, which turned out to decently predict the ...
M Reyes's user avatar
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205 views

MAE vs MSE for linear regression

Several articles says that MAE is robust to outliers but MSE is not and MSE can hamper the model if errors are too huge. My question is that MSE and MAE both are error matrices,our priority is to just ...
Parth Sharma's user avatar
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How to deal with imbalanced categorical variables in regression tasks?

I want to predict real estate prices using several Machine Learning algorithms. My dataset contains numerical and categorical predictors. I already eliminated the outliers of numerical variables. Now ...
moby1209's user avatar
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1 answer
494 views

How do outliers and missing values impact these classifiers?

I am currently working with a bunch of classification models especially Logistic regression, KNN, Naive Bayes, SVM, and Decision Trees for my machine learning class. I know how to handle finding and ...
Vishnu dut's user avatar
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1 answer
135 views

How variable alpha changes SGDRegressor behavior for outlier?

I am using SGDRegressor with a constant learning rate and default loss function. I am curious to know how changing the alpha parameter in the function from 0.0001 to 100 will change regressor behavior....
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Given daily sequence of events with only event ID labels (alphanum strings), what algorithms can be used to detect sequences that are outliers?

For example, the data might be something like this: ...
demoman's user avatar
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1 answer
117 views

Where can I practice multivariate outlier detection?

Can anyone provide me with a dataset, hopefully on Kaggle, where I can practice my skills in outlier analysis? I have been studying this topic for quite a while, but I can't find a case study to apply ...
Mina Ashraf's user avatar
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1 answer
125 views

Word2vec to encode medical procedures when using isolation forests

I am planning to use Isolation Forests in R (solitude package) to identify outlier medical claims in my data. Each row of my data represents the group of drugs that each provider has administered in ...
TheGoat's user avatar
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3 answers
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How to remove outliers properly?

I was wondering what is the best practice for removing outliers from data. Plotting a boxplot for each feature (column of the dataset) and removing data that fall outside the whiskers seems like a ...
Erik M's user avatar
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1 answer
1k views

Explanation of spectral residual algorithm for outlier detection

I've been reading the paper https://arxiv.org/pdf/1906.03821.pdf for spectral residual outlier detection, but I don't quite understand it. Specifically, in the implementation there are three variables ...
ptushev's user avatar
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1 answer
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Outlier detection - missing values

I have a data science challenge in which two datasets are provided, the first one contains weather data (temperature, wind speed, and precipitation) for a number of days, and the other contains flight ...
Alex's user avatar
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1 answer
518 views

How to gps data anomaly detection in python

I have gps format dataset lat, lon. I want to detection anomaly using python. I tested knn, smv, cof, iforest using pycaret. But i did not. These colors anomlay because the angle change is too much ...
ai-mcv's user avatar
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3 votes
2 answers
57 views

How to detect whether an entire series is an outlier relative to others?

I have multiple price series of the same asset as follows. Visually, it is obvious that series "A" (the flat line) is an outlier, and series "E" (the line with the zig-zag pattern)...
finstats's user avatar
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1 answer
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Assign a risk score in records in a dataset

I was wondering, if I have a dataset with categorical and numerical data and labels such as 1 or 0 that shows if a row is anomalous or normal respectively. Is it possible to create somehow a model ...
nameguest's user avatar
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1 answer
92 views

Can I leave natural outliers in a dataset in training?

Can I leave unedited natural outliers in a dataset (outliers that have not appeared just because of mistyping of mistakes in the data)? Or should I also remove them or change them?
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Nonparametric Outlier Detection

Which Nonparametric outlier detection do you suggest to detect outliers (red points) in these plots? I have tested std, IQR, etc., but no good result. It is just one vector including normal and ...
Arkan's user avatar
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1 answer
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Outlier treatment

I am working on a regression problem where I have a lot of outliers in multiple variables. As far as I can think of, there are 3 things I can do to outliers. Remove them (least attractive option) ...
spectre's user avatar
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2 votes
1 answer
817 views

Visualizing outliers using T-SNE

I'm trying to visualize outliers in my data using T-SNE and it seems like the outliers appear as three different clusters. The original data has 7 different columns but I chose to plot the outliers on ...
Sarah Grimes's user avatar
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Should outliers be removed only from the target variable or from any variable where they are found?

What I often do is that I check boxplots and histograms for target/dependent variable and after much caution, treat/remove the outliers. But this is what I do only for the target variable. I.e., if ...
letdatado's user avatar
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2 answers
732 views

Removing outliers from a multi-dimensional dataset & Data augmentation

Removing the outliers of a single-dimensional data can be easily done by removing the points that are outside of the IQR range. But how should the process of detecting and removing outliers be done if ...
Centauri_42's user avatar
1 vote
0 answers
21 views

Methods for comparing datasets for abnormalities

My team at work receives datasets from various companies each quarter, and we're wanting to build some sort of machine learning model that compares the new dataset to our existing data for any ...
Max Herring's user avatar
1 vote
1 answer
7k views

Is standardization/normalization a good way of reducing the impact of outliers when I'm training a machine learning model?

Recently, I have read some papers in which the authors state that they have performed standardization/normalization of the variables for reducing the impact of outliers in the machine learning models ...
Zaratruta's user avatar
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0 answers
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Compare standard deviations in different samples?

I have some data which you can group based on different variables. I know how to test if they have significantly different means. But what the deviation inside the samples?
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