Questions tagged [categorical-encoding]

The tag has no usage guidance.

Filter by
Sorted by
Tagged with
8 votes
5 answers
6k views

How do I encode the categorical columns if there are more than 15 unique values?

I'm trying to use this data to make a data analysis report using regression. Since regression only allows for numerical types, I then need to encode the categorical data. However, most of these have ...
Cinemato's user avatar
5 votes
2 answers
6k views

How to handle categorical variables with Random Forest using Scikit Learn?

One of the variables/features is the department id, which is like 1001, 1002, ..., 1218, etc. The ids are nominal, not ordinal, i.e., they are just ids, department 1002 is by no means higher than ...
Fred Chang's user avatar
5 votes
1 answer
3k views

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

Let's say I have a categorical feature (cat): ...
Xaume's user avatar
  • 182
4 votes
1 answer
725 views

Decision Tree only splits to the left

I can’t really understand, why my decision tree only splits to the left. I originally have 2 categorical features (further named feature 0 and 1), which I concat to one feature since feature 1 is ...
Taitex's user avatar
  • 41
4 votes
1 answer
529 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 ...
carlo_sguera's user avatar
4 votes
1 answer
3k 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 ...
user avatar
3 votes
2 answers
1k 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 ...
Autumn's user avatar
  • 133
3 votes
1 answer
61 views

One hot encoding of target variable containing classes 1 to 9 not including zero

While predicting a solution for a sudoku puzzle using CNN, the target variable should predict values from 1 to 9 for all the 81(9*9) values in the puzzle. Hence the target value shape is (81,9). Using ...
Sathish Kumar SG's user avatar
3 votes
1 answer
1k 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 ...
jakes's user avatar
  • 95
3 votes
0 answers
76 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 ...
PPR's user avatar
  • 171
2 votes
1 answer
31 views

How do I get the mean values that are greater than .5 for my model?

I am trying to build a classification model. One of the variables called specialty has 200 values. Based on a previous post I saw, I decided I wanted to include the values that have the highest mean. ...
bulldog23's user avatar
2 votes
1 answer
563 views

kNN for non-ordinal variables

kNN is a distance-based method, so it requires the input to be in numerical form. I was wondering if it is possible to use kNN imputer for non-ordinal categorical variables (like color). Since the ...
Szymon Adamik's user avatar
2 votes
2 answers
216 views

Categorical Variable Embedding

I have a categorical variable in my labeled dataset. I trained one-hot encoded version of it in another neural network having embedding layer with a larger labeled dataset. I have obtained the weights ...
Agile's user avatar
  • 21
2 votes
1 answer
303 views

Two questions about one-hot encoding: drop first? and features with thousands of categories [closed]

I have two questions about one-hot feature encoding: (1) Is it considered a "best practice" to drop the first (or at least one) one-hot encoded feature when one-hot encoding, like you would ...
Andrew Bell's user avatar
2 votes
2 answers
617 views

Is it acceptable to use label encoding for nominal categorical data when one hot encoding would create too many features?

I'm working on a short data science project to compare the accuracy of different classification methods. The groups decided to use and compare Random Forest, Naive Bayes and SVM. The dataset we are ...
Jim Jones's user avatar
2 votes
1 answer
443 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. ...
Carlos Mougan's user avatar
2 votes
1 answer
23 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: ...
redguy's user avatar
  • 43
2 votes
1 answer
183 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,...
Inês Almeida's user avatar
2 votes
0 answers
197 views

Does it make sense to use target encoding together with tree-based models?

I'm working on a regression problem with a few high-cardinality categorical features (Forecasting different items with a single model). Someone suggested to use target-encoding (mean/median of the ...
KJA's user avatar
  • 21
2 votes
2 answers
168 views

Decision Trees and Categorical Feature Labelling

I am working on a decision tree model and trying to decide how best to handle categorical features. The features in my dataset are generally high in cardinality and I have found that ordinal labeling ...
dbdata's user avatar
  • 21
2 votes
1 answer
289 views

Using the curse of dimensionality for encoding non-ordered (nominal) categorical variables of high cardinality

When the dimension is high, all data are approximately at the same distance away from each other. This makes distance-based methods such as k-nearest neighbors less useful if the data are more or less ...
Vladislav Gladkikh's user avatar
2 votes
1 answer
426 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 ...
Snader's user avatar
  • 21
2 votes
1 answer
353 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 ...
Artur Motruk's user avatar
2 votes
2 answers
2k 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 " ...
Bharathi's user avatar
  • 277
2 votes
1 answer
80 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 ...
user avatar
2 votes
0 answers
327 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 ...
BVB's user avatar
  • 21
1 vote
2 answers
288 views

Encoding features with big amount of classes

Is it worth to encode features with big amount of classes ( such as 60 )? Or should I leave it as it is ?
A Arbitrage's user avatar
1 vote
1 answer
58 views

N-ary decision tree with categorical features

I want to build an n-ary decision tree with categorical features. I am using ordinary ID3 algorithm to build a tree. Lets take the next dataset as a training dataset for building a decision tree: ...
dzi's user avatar
  • 111
1 vote
1 answer
254 views

Encoding for Linear Regression

I have a CSV file with salary information and other columns. I am trying to transform some of these columns into proper values, for a ...
Alix Blaine's user avatar
1 vote
2 answers
4k 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 ...
quilliam's user avatar
1 vote
1 answer
1k 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 ...
revy's user avatar
  • 133
1 vote
1 answer
120 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?
joelpires's user avatar
1 vote
2 answers
616 views

Can decision trees handle Nominal Categorical variables?

I have read that decision trees can handle categorical columns without encoding them. However, as decision trees make splits on the data, how does it handle Nominal Categorical variables? Surely a ...
Connor's user avatar
  • 617
1 vote
1 answer
28 views

How to prevent my model from mistaking categorical feature for ordinal feature

I have tabular data where each group of 100 rows represents a deployment of a specific geometry that has certain features measured. For example, I have 10000 deployments stored in a column called &...
Hamdi Barkous's user avatar
1 vote
1 answer
92 views

If I use Weight of Evidence to transform categorical variables, do I still need to inform their indexes to Catboost

I'm using Weight of Evidence (WOE) to encode my categorical features. Do I still need to inform Catboost that they are categorical features by using cat_features parameter?
Jorge Amaral's user avatar
1 vote
1 answer
250 views

Categorical feature encoding

I am making a classification model. I have categorical and continuous data. The categorical columns include columns with 2 classes such as sex (male, female), and multi-class columns such as location. ...
Alis's user avatar
  • 11
1 vote
2 answers
777 views

Different number of features in train vs test when using Label Encoding

This is not a duplicate of Different number of features in train vs test There are some categorical columns in my data, and the cardinality for each of them is large, so I chose to use LabelEncoding ...
hamnghi's user avatar
  • 13
1 vote
1 answer
1k 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 ...
Casebash's user avatar
  • 113
1 vote
1 answer
34 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 ...
Atish Kumar Dipongkor's user avatar
1 vote
2 answers
5k 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 ...
Maths12's user avatar
  • 506
1 vote
1 answer
128 views

Revisit - Encoding before vs after train test split?

Background: Like a good data scientist, I planned to fit my encoder on my training set and use it to transform my test set. However, when I tried to transform my test set, the encoder threw an error....
Snehal Patel's user avatar
1 vote
0 answers
42 views

Intuition behind catboost encoding techniques

Can anyone please help me in understanding the effect of various bucketing techniques used in CatBoost Algorithm for categorical features? Like there is border, buckets, binarized target mean, counter ...
Mimansa Maheshwari's user avatar
1 vote
0 answers
216 views

Encode each comma separated value in Pandas

I have a dataset ...
spd's user avatar
  • 119
1 vote
0 answers
120 views

Handling date and time fields for classification task

I'm working on a classification task(The dataset is 400,000 rows and 30 columns) and one of my features was date-time. I've extracted the month, day of the week, and hour from the dataset (year is a ...
insomniac's user avatar
1 vote
1 answer
128 views

Categorical encoded variables in scikit-learn diabetes dataset [closed]

When using sklearn.datasets import load_diabetes, the sex variable which is categorical, is scaled to continuous values. Is it even legal to scale such variables?
Ashish Rai's user avatar
1 vote
1 answer
978 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 ...
Bruso's user avatar
  • 111
1 vote
0 answers
324 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 ...
Jacob Niederer's user avatar
1 vote
1 answer
200 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 Can I utilize another encoding? I want to figure out how to include a categorical variable in a model to ...
pierround's user avatar
1 vote
1 answer
51 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: ...
lte__'s user avatar
  • 1,310
1 vote
2 answers
305 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 ...
Mudit Gupta's user avatar