Questions tagged [categorical-encoding]

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How to Present All Categories in All Samples

I have a data contains many categorical columns. When I sampled this data randomly a few times and applied one-hot encoding to categorical columns I noticed that it ended up with datasets with ...
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2answers
59 views

Choose-many categorical features: alternatives to one-hot encoding?

I'm building a model to predict the lifetime value of a client based on the relational data we have on them. The user table has a bunch of one-to-many child tables that might be predictive. Grossly ...
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13 views

Which is the best way to select categorical features with Autoencoders in Python?

I have a dataset containing both categorical and numerical features. I am trying to work with Autoencoders for feature selection, so the first thing I do is to normalise the numerical features. For ...
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1answer
19 views

Handling encoding of a dataset which has more than total 2000 columns

Whenever we have a dataset to be pre processed, before feeding it to the model we convert the categorical values to numerical values for which we generally use LabelEncoding, One Hot encoding etc ...
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1answer
17 views

Encoding of high cardinality multi-label categorical feature?

This is the problem of binary classification: "1" - the subscriber is a driver (belongs to the segment of drivers), "0" - the subscriber is not a driver (does not belong to the ...
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0answers
50 views

Should one-hot encoded categorical features needs to be scaled when used along with text feature while deriving semantic similarity?

My aim is to derive textual similarity using multiple features. Some of the features are textual for which I am using (Tfhub 2.0) Universal Sentence encoder. There are other categorical features which ...
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0answers
31 views

Cat2Vec implementation X = categorical and y = categorical

I am trying to convert categorical values (zipcodes) with Cat2Vec into a matrix which can be used as an input shape for categorical prediction of a target with binary values. After reading several ...
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0answers
16 views

Collapse categorical variable to reduce levels using a decision tree

I am using zip codes as an independent variable as part of a binary classification problem. Naturally, this feature has many different levels (around 2,000), so I was wondering if there is a ...
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1answer
29 views

Encoding categorical data with pre-determined dictionary

in case feature encoding, if I'd like to encode my values based on my pre-determined dictionary, how do I do that? For instance, say, I've values as ...
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1answer
100 views

Target encoding with KFold cross-validation - how to transform test set?

Let's say I have a categorical feature (cat): ...
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0answers
23 views

What is the concept behind the categorical-encoding used in the CatBoost benchmark problems?

I'm working through CatBoost quality benchmark problems (here). I'm particularly intrigued by the methodology adopted to convert categorical features to numerical values as described in the ...
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12 views

tensorflow-embedding-feature-column-with-value

Using this TF tutorial, Classify structured data with feature columns, I was interested in this Embedding columns feature. ...
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1answer
36 views

One-hot & interaction one-hot on multiple categorical

I was wondering if there is any value to creating combined features out of multiple categorical variables when the individual categorical variables are already one-hot encoded? Simple example: there ...
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1answer
94 views

Encoding and cross-validation

Recently I've been thinking about the proper use of encoding within cross-validation scheme. The customarily advised way of encoding features is: Split the data into train and test (hold-out) set Fit ...
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1answer
53 views

On gradient boosting and types of encodings

I am having a look at this material and I have found the following statement: For this class of models [Gradient Boosting Machine algorithms] [...] it is both safe and significantly more ...
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1answer
114 views

What is sum encoding?

I've heard sum encoding mentioned as a method of encoding categorical variables, but I haven't been able to find a clear explanation of what it actually is. I found the following explanation on ...
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2answers
147 views

Why is count encoding effective in improving accuracy? [duplicate]

Can someone please explain why/how Count encoding of categorical features improve accuracy in classification when compared to simply label encoding them ? I found one explanation in kaggle " ...
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1answer
62 views

Is this attribute numeric or categorical (ordinal)? Help!

So I have this dataset I need to perform several techniques on as part of a data mining/machine learning project of some sort in PYTHON. There are a couple of features however, that have me very ...
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2answers
22 views

Categorical and non-categorical data in the same column

I have a unique dataset that has many columns and most columns contain both categorical and non-categorical data. For example, let's say that one column is attribute_1 and for observations that have ...
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16 views

Correlation between features in python

I have a dataset which has categorical variables as features. They are nominal in nature. One of the variable has 312 categories. I want to check how correlated the variables are, to check ...
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7 views

Predict values if will be above a threshold (so bottomline behaviour)

this time I am not asking help with coding but on the concept level. I have trained a classifier (RF but this is not important) that provide goods accuracy (around 90%)
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21 views

Autoencoder to encode features/categories of data

My question is regarding the use of autoencoders (in PyTorch). I have a tabular dataset with a categorical feature that has 10 different categories. Names of these categories are quite different - ...
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1answer
101 views

sklearn serialize label encoder for multiple categorical columns

I have a model with several categorical features that need to be converted to numeric format. I am using a combination of LabelEncoder and OneHotEncoder to achieve this. Once in production, I need to ...
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1answer
29 views

Categorical data - how to handle [closed]

Few questions on categorical data. Need suggestions / pointers: How can we check for correlation between categorical features and target or between the features themselves? How about correlation ...
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1answer
69 views

Implementing sklearn's FeatureHasher on Unseen Data

For a little bit of background I have been working on a binary classification of health insurance claims and am implementing sklearn's FeatureHasher to vectorize categorical features, many of which ...
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120 views

Positional Encoding of Categorical Features in a Time Series Transformer

I am training a Transformer for Multivariate Time Series prediction. I am working with Categorical features and I am thinking of using Positional Encoding to encode them instead of Embedding. Has ...
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1answer
21 views

Which ML classifier is appropriate for me if all of my features are categorical?

My dataset contains four features. All of the features are categorical. There are 150 categories in the value of 1st and 2nd features. There are 8 categories in the value of 3rd and 4th feature. I ...
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12 views

Best Practices for Boosting, Trees, Random Forests re. Number of classes in a feature and number of samples per class

I'm doing a regression to predict house prices using multiple algorithms and the h2o platform. In particular, I'm using a variety of GBM, DRF, and GLMs. I have a large amount of data from several ...
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2answers
661 views

what should i do if my target variable is categorical when using decision tree? (many categorical variables)

all, i'm trying to classify a set of features to belong to a particular company (my dependent variable). my independent variables are a mixture of continuous and categorical features. my data-set ...
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1answer
100 views

Categories with the same mean in target encoding

While doing target encoding it can happen that two categories have the same target mean. This is bad because there will be no difference in the new feature in it and we will lose some information. ...
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0answers
58 views

Implementing Scikit Learn's FeatureHasher for High Cardinality Categorical Data

Background: I am working on a binary classification of health insurance claims. The data I am working with has approximately 1 million rows and a mix of numeric features and categorical features (all ...
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26 views

Target Encoding and Feature Scaling

I am using Support Vector Classification which performs well when we have done Feature Scaling, however, I am using Target Encoding on my categorical variables. Is it advisable to do Feature Scaling ...
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1answer
51 views

How to do target encoding when data has repeated rows?

How can I do encoding for a category when data has repeated rows? Can I do target encoding? Or Is there another encoding I can use? I want to figure how to include a categorical variable in a model ...
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1answer
54 views

What to do if a specific label of a category appears only a few times?

Let's say I am trying to predict whether a car will be auctioned or not (not what I'm actually trying to do, but it represents it pretty well) using tabular data. I have the year the car was made, its ...
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1answer
26 views

What is the best way to encode an arbitrary collection of strings into int categorical variables?

I have a bunch of categorical labels which I want to transform into int categorical features for an ML algorithm. The problem is I don't have a prior list of the categories, so that I can't just ...
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1answer
33 views

Can we optimize regression problems that have categorical variables by encoding them if on the other hand we are inserting multicollinearity? [duplicate]

Can we optimize regression problems that have categorical variables by encoding them if, on the other hand, we are inserting multicollinearity?
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1answer
19 views

Memory efficient encoding logic for group categories

I have a huge dataset with categorical data. It is comprised of alerts having multiple properties. Each alert belongs to a group, and some even belong to multiple groups. It looks somewhat like this: ...
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288 views

(Incremental) PCA for all data

I'm using PCA to find prime components that are covering most of the variance in my dataset. What I usually do is I run a PCA for all components, then see how many components cover most of the ...
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1answer
24 views

LabelEncoder with a Multi-Layer Perceptron?

So we're working on a machine learning project at work and it's the first time I'm working with an actual team on this. I got pretty good results with a model that uses the following SKLearn pipeline: ...
2
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0answers
120 views

Multi-valued categorical features in LIME

I am working with the LIME implementation by Marco Ribeiro (https://github.com/marcotcr/lime). Specifically, I am utilizing the LimeTabularExplainer as I have a mixture of numerical and categorical ...
2
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1answer
35 views

Different encoders applied to a dataset

I have a dataset which have both categorical features with high cardinality (>8000) and low cardinality (4 or 5). Would that be ok to encode the high cardinality ones with one encoder (target encoder,...
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2answers
182 views

Dummy encoding the categorical variables using the changed version of OneHotEncoder [duplicate]

This is my code, I was trying to dummy encode the first column of X using OneHotEncoder but it was showing error and the documentation page of OneHotEncoder says that it has been changed and I wasn't ...
3
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1answer
197 views

Strategies to encode categorical variables with many categories

I was going over the Kaggle competitions IEEE,Categorical Feature Encoding Challenge and one of the ways in which categorical variables have been handled is by replacing the variables by the ...
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0answers
62 views

Categorical Encoding with Helmert Coding and James-Stein Encoder

I was going through the coding categorical variables documents and came across the following things: Helmert Coding James-Stein Encoder Can somebody explain the in-detailed implementation of these ...