Questions tagged [categorical-encoding]
The categorical-encoding tag has no usage guidance.
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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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NAN in keras neural network results
I am creating a neural network simple architecture. But I keep getting NAN in result, cant figure out why, below is my code.
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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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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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What are the options/best practices for encoding categorical features for multilabel classification?
I am working on a multilabel classification problem with both continuous and categorical features. For a single label problem, I might make use of a supervised encoder for my categorical features such ...
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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 ...
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What is the best practice to normalize/standardize imbalanced data for outlier detection or binary classification task?
I'm researching Anomaly/outlier/fraud detection, and I'm looking for the best practice to pre-process the synthetic data for imbalanced data. I have checked all methodology for normalizing/...
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What's the minimum percentage of categories should be present in the categorical variable for to ignore the variable entirely
For example, if i have a feature "colour_codes" that has close to 5000 distinct color codes inside it. And the number of samples/rows is 10 million. Then should I ignore the feature "...
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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 ...
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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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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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Collapsing categorical data into more than 3 categories
I have a bunch of categorical, part of speech data that I want to collapse into fewer categories. np.where() won't do because I want to have 6 categories at the end: noun, verb, adjective, adverb, ...
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Turning multiple binary columns into categorical (with less columns) with Python Pandas
I want to turn these categories into values of categorical columns. The values in each category are the current binary columns present in the data frame. We have : A11, A12.. is a detail of A1 so if ...
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How to encode Income Type Ordinal Data into numbers?
I am doing a mini project on Credit card Approval Prediction. The Dataset I have taken is from Kaggle: https://www.kaggle.com/datasets/rikdifos/credit-card-approval-prediction
Problem: I want to ...
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Encode multiple label categorical variables with consideration of the frequency and standardization
I'm currently working on a dataset containing, among other features, multi-labeled categorical data per person for the last 3 years. I'm not sure how to handle this kind of categorical data where ...
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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 ...
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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 ?
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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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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 ...
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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 ...
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Encoding of categorical variables to reduce the effect of erroneous labels
I have a structured dataset containing (nominal) categorical variables encoded as labels, let's say a feature includes labels from 1 to 20. Some of the labels in that feature could just be errors, ...
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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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Multi-class classification model unable to return desired outcome
I have a scenario of multi-class dataset with around 10 distinct classes of target. There are 3 categorical features each with multiple labels. If we check the data, each unique combination of feature ...
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How do I use one hot encoding with 240 nominal variables and each with equal occurrence?
The method I saw that's generally used to deal with large # of nominal variables is to keep the most frequent variables and introduce a new "other" category. But that's not possible with my ...
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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 &...
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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....
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How to encode data set with all categorical predictors
I have a data set with all categorical predictors. They are 14 in number. If I do one-hot encoding, I would be getting more than 35 new features, which I think is not right due to the curse of ...
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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:
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Binary classification works with softmax, but not sigmoid
I am doing a binary classification problem for seizure classification. I split the data into Training, Validation and Test with the following sizes and shapes
dataset_X = ...
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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 ...
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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 ...
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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?
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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 ...
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Encode and replace missing categorical values
Should replacing missing values for categorical variables be done after one-hot-encoding? (ex: I have a column named Car Type: Sedan, Hatchback, MPV, SUV) which has 5% missing values.
Would there be ...
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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.
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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 ...
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Dealing with observation with arbitrary number of categories with arbitary number of values
Suppose to have a set of elements $X = \{x_1, x_2, ..., x_n\}$. Each element is characterised by a set of features. The features characterising a particular element $x_i$ can belong to one of $q$ ...
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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 ...
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How to deal with address (like zip-code) for training a model?
To me it doesn't make sense to normalize it even if it is a numerical variable like Zip Code. An address should be interpreted as categorical features like "neighborhood"... ?
Suppose I have ...
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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. ...
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Target encoding with KFold cross-validation - how to transform test set?
Let's say I have a categorical feature (cat):
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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 ...
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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 ...
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Separating numerical and categorical features in a binary classification problem
I have a dataset with employee data with around 9500 rows, and have to predict if the target is 0 or 1.
Some of my features are the department of an employee, gender, salary, review_score(numerical),...
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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 ...
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Dealing with Extra Categories in Test Set
Suppose I have a data set which consists a dependent variable y and independent variables X. Suppose that there is a specific ...
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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 ...
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