Questions tagged [categorical-encoding]
The categorical-encoding tag has no usage guidance.
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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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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 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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179
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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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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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How to change label values in mask image
I have a mask image, where 0,10,20,30 are the labels assigned to the image. I want to change that value to 0,1,2,3 for multiple images.
0 - background
10- Building
20- Forest
30- Road
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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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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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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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OHE vs dummy variables
Could someone explain to me the difference between creating categorical “embeddings” with StringIndexer and OneHotEncoder, vs just creating dummy variables for each category? Aside from it being more ...
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Irreversible Hash Encoding vs Reversible One-Hot Encoding
I understand that hash encoding for categorical features allows us to restrict or limit the dimensions of our dataset, which one-hot encoding does not.
I was wondering how we could compare these two ...
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48
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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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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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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 to create the categorical mask for images specifically for Tensor? Or port the NumPy function correctly to Dataset.map function
I'm trying to move from NumPy array as my dataset to tensorflow.Dataset.
Now, I've created a pipeline to train the model for classification problems. At some point, I just normalize all the images ...
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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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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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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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111
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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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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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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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203
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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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120
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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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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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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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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 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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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 ...
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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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Handling categorical data with more over 100 unique classes
I am working with a pure categorical data set. And some classes have more than 100 unique values.
I could not find any appropriate encoding possibility. So I created a SQL table, where each value got ...
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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?
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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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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 ...
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445
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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 ...
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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 ...
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Tensorflow - I don't get the right shapes - `ValueError: Shapes (9, 1) and (8, 9) are incompatible`
I want to train a Sequential Neural Net (NN) with Tensorflow.
...
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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 ...
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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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why should i do target encoding within cv loop?
i wish to use target encoding, using the category encoders sklearn library. I don't really understand why it is necessary to include this as a step in a sklearn pipeline WITHIN the cross validation ...
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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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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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