Questions tagged [categorical-data]
Categorical data can take on a limited (usually fixed) number of possible values called categories. Categorical values "label", they do not "measure". Nominal and dichotomous/binary scale types are categorical. Some people consider ordinal scale categorical too.
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Feature selection on datasets with both categorical and numerical features
I'm proposing a novel methodology for feature selection in the context of tabular datasets that contain both numerical and categorical features. In order to prove the efficacy of my methodology, I ...
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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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classification using simple relationships between time series data
I am looking to predict which courses are taught by which university professors at my school. More specifically, for each semester and professor I want to know the probability breakdown of which ...
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Combining Textual, Categorical and Numerical data for Semantic Search using SentenceTransformers model
I'm building a food semantic search model and I want to use a pre-trained SentenceTransformers model with cosine similarity. I'm using Epicurious dataset for the corpus which consists of textual (&...
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Training Biased/Uneven Categorical Data with CatBoost, Unbalanced/Unseen Categories Handling
Summary:
I am training a discount eligibility model where the dataset represents historical data for products where people availed discounts based on simple features like product category, discount ...
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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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What are some standard methods for studying co-ocurrence patterns?
We have a series of academic articles annotated with several tags eg "environmental issues", "legal issues", etc.
I wish to understand whether some of these topics are frequently ...
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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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Unsupervised rule extraction of categorical data
I have a dataset of network traffic with three features that I would like to extract rules from in order to apply firewall/flow control rules i.e. the permitted flows.
I am able to classify a ...
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Mixing categorical data and time-series data for regression purpose
I came across a problem and I have been looking on internet how to solve it without finding a solution that fits my need. I am trying to predict investors behavior. To be precise, I would like to ...
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Converting categorical to the percentage
How do I convert the categorical value to the percentage?|
I have this asset wellness data:
Poor: 3
Warning: 27
Good: 120
How do I convert it to the percentage ...
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When using Chi-Squrare test in feature selection makes sense?
What are the prerequisites that need to be fulfilled before conducting a chi-square test (Bivariate analysis)? For instance, before having a correlation matrix, we should first ensure linearity. What ...
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Feature Selection - determining the significance of imbalanced categorical data column
I have a dataset with a categorical column that contains three categories. One of the categories represents 98% of the data, while the remaining 2% are distributed between the other two categories, ...
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Interpretation of best subset selection regression model for factor variables with more than 2 levels
I applied the best subset selection regression model in R from leaps package to my dat dataframe. ...
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Help me identify the type of plot and the relationship between the dependent variables
Question: I am not sure how to describe the sample graph attached. Can you please help me identify the type of plot and how to statistically measure the relationship between the dependent variable (Y-...
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Clustering with features of categorical type
I have a dataset of 17 features and class label for each datapoint. The description of dataset is as follows:
2 features contain values 0, 1, 2, 3
15 features contain values 0, 1, 2
class label ...
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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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Best practice for variables that only have answer if yes in previous column
I currently have a dataset that consists of survey data that has several columns that have answers dependent on the previous question. For example, I may have a question that says "Did you take ...
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What is the best way to whittle down rendundant categorical data?
I'm trying to build a linear regression model in Tensorflow (and preprocessing with pandas) that will help me categorize bank transactions. I'm trying to whittle down the vendor parameter, because the ...
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How to efficiently reduce dimensions of one-hot encoded categorical values?
I'm currently working on a project where I'm using an LSTM to learn and predict sequences of categorical data.
My dataset consists of variable-length sequences of items $s_i = [x_{i_0}, x_{i_1}, ..., ...
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Finding a relation between three variables
I am new to data mining and have learnt about association rules mining, classification analysis, cluster analysis and outlier analysis. So, to find relationship between three variables, regression ...
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Data Leakage possibility related to replacing rare values on a categorical column
I am exploring the possibility of data leakage for categorical columns, replacing rare values with category 'Other'
Let's say I have a DF with 40 categorical columns. I will check each of them, find ...
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How to make a predictive model using a timeseries data consisted of binary information?
I have a set of data that is showing the state of an object as a function of time. I would like to know what and how I should be utilizing machine learning to predict the state of the object at some ...
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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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How to do forecasting with categorical timeseries?
I have a dataset that is in the form of categorical timeseries: (specifically, we either know or don't know the values of 6 degrees of freedom of an object at any given time). If we know it, it's ...
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Categorical Dataset Machine Learning
I have a dataset that is completely binary and labeled. I would like to be able to use machine learning for one of the columns. I have read that unsupervised models, such as K-Means, do not work with ...
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Customer Segmentation with mixed data [closed]
I want to perform clustering. I am reading about this topic but I am totally confused. My dataset has 490 observations and it consists of numerical data (3 columns: Recency, Frequency, Monetary), ...
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Manual computation of the predictions in a convolutional neural network
I am trying to manually compute the predictions of the Keras library for a convolutional neural network. However, I am struggling a lot to match my final result with the ones provided by Keras. I do ...
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How to predict probability of an event when we have a month to month data?
I'm trying to find references about how to proceed to get the probability of an event happening when we have "temporal data" in our table
My data is basically:
hex_id: id of the object
date:...
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How to detect Novelty from different ranges of target variable?
I've a dataset of multiple categorical columns along with a target column that is continuous. Assume combination of categorical columns has a different range of values of target. Ex
Col1 - col2 - col3 ...
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How to do DBSCAN clustering with mixed variables (numerical features and binary/ordinal variables)?
I have a question written at the end of the post which refers to the "Distances" paragraph. The other first two paragraphs give additional info.
Context
I'm working on a project where I have ...
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Encode categorical data for unsupervised learning
What is the best encoder for categorical data in unsupervised learning?
I am using unsupervised learning on mixed data (such as K-means).
Before running my unsupervised algorithm, I am using dimension ...
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Clustering mixed type variables with Orange
I wonder if with Orange it is possible to cluster mixed type data, so a dataset with numeric as well as discrete (categorical) data (ordered / unordered). Can you show an example of how that could be ...
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Is there a procedure for determining if a classification problem is ill-defined?
Consider a group of objects denoted $O = \{o_0, o_1, \cdots\}$ where each object is associated with a feature vector $F = \{f_0, f_1, \cdots\, f_{N-1}\}$. For this case, assume the features are ...
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How to use categorical data in forecasting with Prophet?
I'm trying to create a model to predict the number of players on a video game at a certain time and was trying to figure out how to integrate categorical data into my forecasting problem. So far, my ...
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Does Scikit-Learn's OneHotEncoder make all Columns Categorical?
I've been using Scikit-Learn's OneHotEncoder to turn categorical data into binary columns, however, it seems that fitting ...
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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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What Would Be a Good Measure of Feature Importance in Regression?
Doing simple supervised regression where the label is a floating point number (guaranteed positive) and the features are a mix of continuous floating point values and some categorical features.
What ...
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Dealing with categorical columns with unbalanced value count
I'm doing some data processing and wondering what is the best practice for dealing with categorical columns that has a value counts plot looking like the below (these are one-hot-encoded at a later ...
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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 do well informed labels for ordinal encoding improve model performance?
From Kaggle's intermediate machine learning tutorial, it was stated that
for each column, we randomly assign each unique value to a different integer. This is a common approach that is simpler than ...
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How to predict on data that is label encoded as end user will input a categorical data?
My dataset contains about 29 features with 3 class labels as result. Among these 29 features around 24 features are categorical i cannot transform each category into numbers as there are many more ...
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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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working principle of Support Vector Machine
I have a dataset consisting of numerical features and categorical features. I want train the training set using SVM. SVM is a quadratic optimization algorithm. I would like to know the how SVM works ...
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How to cluster data where each sample has a different amount of different features?
I decompiled one million binaries and stored a representation of every function in a database.
Each binary has a list of functions.
Each function has a number of callees (how often its called by ...
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SkLearn Categorical Naive Bayes Vs Mathematical theory of Naive Bayes
The Naive Bayes classification based on the following formula
$P(C_i|X) = {P(X|C_i)P(C_i) \over P(X)} ... i)$
$P(X|C_i)$ is the posterior probability of $X$ conditioned on $C_i$, $P(X)$ prior ...
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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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Do you use categorical data types?
Personally I've never used the categorical data type in pandas, and leave eveything as objects.
I've seen it has the capability to be saved as parquet files, saves data etc...
What are the pros and ...
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