Categorical data pytorch. Jan 12, 2020 · A little background on Pytorch.

Categorical data pytorch Aug 15, 2022 · What are some common mistakes when using to_categorical in Pytorch? to_categorical is a function that converts numerical values into an one-hot encoded vector. Mar 19, 2023 · Hi, I intend to build a multi-class classifier using a transformer. I have a tabular dataset with a categorical feature that has 10 different categories. randn(2,64), requires_grad Jan 2, 2023 · Hi! I am using a PPO2 agent for RL. NLLLoss from PyTorch; Summary. This is often more memory-efficient and can be used with various PyTorch operations. So I decided to fill that lacuna with my own post. 5 to 3. a length-2 Tensor with a 1 and a 0 depending on the sex). The idea of this post is inspired by “Deep Learning with PyTorch” by Eli Stevens, Luca Antiga, and Thomas Viehmann. List of categorical features indices. np_utils import to_categorical labels123 = to_categorical(y_pred_arr, dtype = "int64") however, got this error: IndexError: index -75 is out of bounds for axis 1 with size 74 PyTorch Frame is a deep learning extension for PyTorch, designed for heterogeneous tabular data with different column types, including numerical, categorical, time, text, and images. JoshP February 3, 2021, Oct 20, 2021 · Hi , I am trying to implement swap auto-encoder with one of categorical column,in following data column “Name” is categorical and rest of first 3 columns are numerical This research paper applies a feedforward neural network model in PyTorch to a multiclass classification problem using the Shelter Animal Outcome dataset and explores feature importance using two common techniques: MDI and permutation. w0 = torch. Sep 18, 2024 · Here’s the deal: the torch. int64) categorical_data[: 10] Free eBook: Git Essentials Check out our hands-on, practical guide to learning Git, with best-practices, industry-accepted standards, and included cheat sheet. Jun 12, 2020 · a bit late but I was trying to understand how Pytorch loss work and came across this post, on the other hand the difference is Simply: categorical_crossentropy (cce) produces a one-hot array containing the probable match for each category, sparse_categorical_crossentropy (scce) produces a category index of the most likely matching category. 2 Inside the main there is a for loop keeps Mar 28, 2018 · Suppose we have two kinds of input features, categorical and continuous. Loading data for timeseries forecasting is not trivial - in particular if covariates are included and values are missing. Involve me and I learn. This post is a guide for using embeddings to represent categorical variables in machine learning algorithms. The categorical data may be represented as one-hot code A, while the continuous data is just a vector B in N-dimension space. Dec 8, 2018 · Traditionally, the best way to deal with categorical data has been one hot encoding Training Your PyTorch Model to Count. Both are vector representations for categorical variables. image_frame. shape = (100, 60, 4), [batch, length of sentence, features per word] output. It creates a learnable vector representation of the available classes, such that two similar classes (in a specific context) are closer to each other than two dissimilar classes. Alternative Representations PyTorch offers more efficient ways to represent and manipulate categorical data: Integer Encoding Assigning unique integer values to each category. 8. tensor(np. BCELoss from PyTorch; nn. Torch Distributions and torch. It's not so difficult at all, to be frank, so make sure to read on! What you see is obviously the categorical crossentropy formula. It offers a modular framework for implementing existing and future methods. If i use one hot vector to encode the genre , Action can be Nov 29, 2020 · I want to compute the (categorical) cross entropy on the softmax values and do not take the max values of the predictions as a label and then calculate the cross entropy. PyTorch Forecasting provides the TimeSeriesDataSet which comes with a to_dataloader() method to convert it to a dataloader and a from_dataset() method to create, e. This seems overly complicated Demystifying Categorical Distributions in PyTorch: A Guide to torch. You want to predict the next temperature based on historical data. Aug 25, 2021 · I am working on a problem where I have to try to predict the dependent variable (continuous) every hour based on hourly temperature (the single continuous variable in predictor space), along with 4 categorical variables, i. Jun 8, 2023 · My features are a mix of univalent/multivalent dense & sparse categorical string, and univalent/multivalent dense & sparse categorical int. I have been learning it for the past few weeks. The former is a sparse representation where only one of the values of each vector representation is 1 rest being 0. cat_dims: list of int (default=[] - Mandatory for embeddings) List of categorical features number of modalities (number of unique values for a categorical feature) /!\ no new modalities can be predicted. While it’s commonly associated with tasks like image and text analysis… Feb 10, 2020 · Here we can see that we have four major groups: group 1, with Monday and Tuesday, possibly related to a low amount of sold beers, due to being the start of the week; group 2, with Wednesday and Thursday, with some distance from group 1; group 3, with Friday and Saturday, relatively close to group 2, indicating that they show more similarity than when compared with group 1; and group 4, with May 23, 2023 · Hi, I am creating a LSTM model where categorical features need to be embedded before using it in the LSTM. DataLoader. cat_emb_dim: list of int (optional) List of embeddings size for each categorical features. PyTorch Recipes. Because the dataset in this study [3] by K. This is the code. # Convert the NumPy array to a PyTorch tensor with a specific data Apr 29, 2018 · This data set (like many data sets) includes both categorical data (such as the state the store is located in, or being one of 3 different store types) and continuous data (such as the distance to the nearest competitor or the temperature of the local weather). Where the presence of a 1 in the ‘N’th category indicates that the observation belongs to that category. I want to pass information about age and sex along with the image. , an hour of the day, day of the week, month, and year (as shown below). """ def __init__( self, data_dir_path: str, target_col: str, numeric_cols: List[str A clean and robust Pytorch implementation of Categorical DQN (C51) - XinJingHao/C51-Categorical-DQN-Pytorch Nov 30, 2020 · Hey Folks, I was just trying to understand the Pytorch Embedding layers. Transformer? Or should I write the transformer by myself especially the decoder? This is because the example in Pytorch Transformer suggests the ‘tgt’ is also a 3D array. I have made this easy code snippet and because I use the argmax of the output tensor as the targets, I cannot understand why the loss is still high. You can either: Combine the auxiliary features with the time series data (what you suggested here). MSELoss from PyTorch; nn. kl_divergence. These values should be non-negative, finite, and sum to 1. 7. Feb 6, 2021 · If your inputs contains categorical variables, you might consider using e. Pytorch is a popular open-source machine library. Whats new in PyTorch tutorials. Represent the categorical data as 1-hot vector then concatenate it to the flattened input to the last classification layer. bool. One important thing to mention before I move on, common to all models, is that pytorch-widedeep models (in this case TabMlp) do not build the last connection, i. Apr 8, 2023 · Below are the documentations from PyTorch that give you more details on how the various loss functions are implemented: nn. Not sure how to shape the data if I am batching sentences of similar length. Feb 3, 2021 · PyTorch Forums Embedding categorical similarities with continuous data. should I transform Jun 9, 2021 · I'm currently working on a Deep reinforcement learning problem, and I'm using the categorical distribution to help the agent get random action. I have tried one-hot-encoding them all but this resulted in an explosion of features (resulted in over 120 features which made my data very sparse). 6 Operating System: Darwin 18. I am looking for advice on what’s the most efficient way to load in the dataset. 2], [0. vision. nn. random. Aug 9, 2023 · PyTorch provides excellent support for GPU acceleration and pre-built functions and modules, making it easier to work with embeddings and categorical variables. Jul 11, 2019 · Let’s say i have a data field named movie_genre for each sample movie , it is selected from the following genres: Action Adventure Animation Comedy And for each movie , it might contain multiple genres: mid genres 1 Action | Adventure 2 Animation 3 Comedy | Adventure | Action which means, the movie’s genres is a variable list. In fact, each str feature can be considered as a sentence with specific number of vocabs and I need to define their index with torch. The fastai library lets you enter both categorical and continuous variables as input Jan 21, 2021 · X = [[0. Keeping ease of useability in mind, PyTorch Tabular accepts dataframes as is, i. Hashing(vocab_size)(feature) embedding = tf. PyTorch is the premier open-source deep learning framework developed and maintained by Facebook. int64, and torch. Intro to PyTorch - YouTube Series Run PyTorch locally or get started quickly with one of the supported cloud platforms. Jan 12, 2020 · A little background on Pytorch. I have some time series variables, such as the year, month, week number, and day, as well as some spatial variables including US State and county number. Run PyTorch locally or get started quickly with one of the supported cloud platforms. Categorical Embeddings¶ The CategoryEmbedding Model can also be used as a way to encode your categorical columns. and data transformers for images, viz. Embedding takes tensor containing the indices as input, but not one-hot vector. 7 x86_64 Expected behavior I executed code from the [TFT Tutorial] with a preconditionning of my predictors into categorical like Aug 10, 2023 · Hi, I’d need expertise for the following problem… thanks a lot, your help is very appreciated! I am dealing with a dataset for which: Samples have 10 continuous features: x = (x1, x2, …, x10) certain samples often have a missing (continuous) feature x1, that ranges say from 0 to 100. loc[idx, numerical_columns], it now concatenates just fine now and runs. Categorical . Code for processing data samples can get messy and hard to maintain; we ideally want our dataset code to be decoupled from our model training code for better readability and modularity. Embedding Nov 2, 2024 · PyTorch KL Divergence Essentials. DataLoader and torch. Deep learning has received much attention for computer vision and natural language processing, but less for tabular data, which is the most prevalent type of Jul 10, 2019 · However, the pytorch embedding layer nn. Simply encoding the categorical values (e. preprocessing import LabelEncoder labelencoder= LabelEncoder() #initializing an object of class LabelEncoder data['C'] = labelencoder. Each of the images has sex and age categorical data associated with it. Sex is categorical and can be represented by a one-hot encoding (i. What it Does. Learn the Basics. Categorical (n: int, shape: Optional [Size] = None, device: Optional [Union [device, str, int]] = None, dtype: str | torch. Custom PyTorch Models Custom PyTorch Models Implementing New Supervised Architectures Other Features Other Features Using Neural Categorical Embeddings in Scikit-Learn Workflows Using Neural Categorical Embeddings in Scikit-Learn Workflows Table of contents Baseline CategoryEmbedding Model Apr 16, 2019 · Categorical variables: These are discrete variables which is used to split the data based on certain characteristics. What is the best way to get the loss - i. I’m unsure what the alternatives would be and if passing these values to the model might even work in your case. These Jun 20, 2020 · Creating a part of speech LSTM. By changing numerical_data = self. Mar 15, 2021 · Representing categorical data continuously, like this continuous view of the savanna. Names of these categories are quite different - some names consist of one word, some of two or three words. Bite-size, ready-to-deploy PyTorch code examples. compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['categorical_accuracy']) Note that all the layers in the keras network have been implemented with L2 regularization kernel_regularizer=regularizers. Oct 29, 2019 · Hello Everyone, I am facing a problem of semantic segmentation of 2D data. I am planning to encode the categorical variables as embeddings. Mar 16, 2021 · It seems you are not normalizing the loss via dividing by the used weights as seen here. Mitrović, D. keras. 1 Transforming Boolean Values. Embedding layer, which would transform the sparse input into a dense output using a trainable matrix. See all from David Heffernan. Embedding. Aug 14, 2015 · For converting categorical data in column C of dataset data, we need to do the following: from sklearn. 88 and 89 are closer together than 1 and 88 are, so this data is more continuous than categorical). It is common that in the same image more regions should have that label, and other parts of the image should have one of the other labels as well (which is not done). And all this can be done using a scikit-learn style Transformer. l2(5e-4) , also I used he_uniform initialization which I believe is Aug 29, 2022 · Hi there, I have my preprocessed dataset splits in Parquet files on GCS. e. 3], …] - (x,y) input data y = [[1,0,0], [0,1,0], … ] - one-hot encoded output. The input data is an audio spectrogram for example MFCC in (batch, steps, features). But all in all I have 10 unique category names. While random forest classification is a powerful machine-learning technique, it typically requires numerical input data. instead of using a One-hot encoder or a variant of TargetMean Encoding, you can use a learned embedding to encode your categorical features. shape = (100, 60, 10) [batch, l… Sep 28, 2020 · The process binarizes categorical data with ‘N’ distinct categories into N columns of binary 0’s and 1’s. About Implement Categorical Variational autoencoder using Pytorch Feb 22, 2021 · Snippet 3. You can create a Categorical distribution by providing either: probs: A tensor containing the raw probabilities for each category. utils. I am creating a custom dataset to pass to the model. Preprocess Pandas DataFrame Categorical Columns. 1 Python version: 3. In this blog post, I will go through a feed-forward neural network for tabular data that uses embeddings for categorical variables. In this post, you have seen loss functions and the role that they play in a neural . I am amused by its ease of use and flexibility. Examples: Classifying an image as one of several categories (e. Greconici, has a large number of Categorical Embeddings for Tabular Data using PyTorch Yogita Narule, Shilpa Lambor, Sanskruti Khedkar, Prathamesh Berad Department of Multidisciplinary Engineering, Vishwakarma Institute of Technology, Pune, 411037, Maharashtra, India A In Pytorch there is no readily available Gumbel Softmax distribution to sample from, so have to implement the Relaxed Categorical representation to sample the latent representation. layers. In this article, we will explore the best practices for data preprocessing in PyTorch, focusing on techniques such as data loading, normalization, transformation, and augmentation. After that, I want to concatenate the metadata with these features and pass them to the final layer as shown below Now the problem is since the neural network takes only numeric values, how can I pass the categorical Aug 31, 2023 · categorical_data = torch. PyTorch tensors support various data types, including torch. categorical. # Create a dictionary to store the embedding dimensions embedding_dims = dict(zip(categorical_features, embedding_sizes)) class LSTMModel(nn. Embedding class in PyTorch is your go-to tool for embedding categorical data. The only solution that I could think of is to have separate output layers (softmax) for each categorical variable. Contribute to jxmorris12/categorical-vae development by creating an account on GitHub. I define the data… Aug 13, 2020 · I saw a sudoku solver CNN uses a sparse categorical cross-entropy as a loss function using the TensorFlow framework, I am wondering if there is a similar function for Pytorch? if not could how could I potentially calculate the loss of a 2d array using Pytorch? [Pytorch] Minimal implementation of a Variational Autoencoder (VAE) with Categorical Latent variables inspired from "Categorical Reparameterization with Gumbel-Softmax". Embedding in PyTorch 3 How to use Embedding layer for RNN with a categorical feature - Classification Task for RecoSys Apr 2, 2022 · Age data is already numeric (I don’t think it’s quite categorical since proximity matters, i. May you let me know how the build the same line of code in order to work? Jul 12, 2017 · I have a dataset where features are of different types, such as float32 and str (categorical). Now I am using NumPy. 1. loc[numerical_columns] to numerical_data = self. (default =1) Feb 17, 2021 · PyTorch-Forecasting version: 0. keras. For example, the variable may be “color” and may take on the values “red,” “green,” and “blue. Sep 29, 2020 · I have a network which outputs a vector of length two. 3 after 100 epochs (this is for chunk 1 for example sake). When we build a ML model more often then not it is required for us to transform the categorical variable before we can use it in the A partial implementation of Continuous Diffusion for Categorical Data by Deepmind, in pytorch. 9, nesterov=True) resnet. Dec 5, 2024 · 10 min read · Dec 5, 2024-- When converted into categorical data, you can apply categorical crossentropy: Don't worry - it's a human pitfall to always think defensively when we see maths. def choose_action(self,enc_curren Run PyTorch locally or get started quickly with one of the supported cloud platforms. In this research paper, we apply a feedforward neural network model in PyTorch to a multiclass classification problem using the Shelter Animal Outcome dataset. I Dec 11, 2023 · Hello everyone, I’m encountering a peculiar issue with my TimeSeriesDataSet in PyTorch Forecasting. Mar 12, 2024 · Data preprocessing is a crucial step in any machine learning pipeline, and PyTorch offers a variety of tools and techniques to help streamline this process. What I am trying to do is Keras equivalent of: hashing_layer = tf. Familiarize yourself with PyTorch concepts and modules. For example, y could be [y1, y2, y3, y4, y5] and each y_i is a categorical variable with multiple classes. Jul 22, 2019. I Predictive modeling with deep learning is a skill that modern developers need to know. Teach me and I remember. So how should i encode the data so that it can be fetched into the embedding layer? Jan 15, 2019 · What is categorical data? Before diving into ways of handling categorical data and passing it to a neural network, I want to loose a few words to describe what it is. Therefore, encoding categorical variables Data#. If you’re unfamiliar with the basics or need a revision, here’s a good place to start: Run PyTorch locally or get started quickly with one of the supported cloud platforms. Both my NN and also the agent itself are using categorical distribution. distributions. a validation or test dataset from a training dataset using the same label encoders and data May 12, 2023 · Hallo, I have a problem understanding how I can define a Dataset/DataLoader combination that processes batches of a custom size. The entire dataset won’t fit in memory. Currently, this is what I have: class TabularDataset(IterableDataset): """Class for loading tabular data. an nn. May 21, 2020 · My question is regarding the use of autoencoders (in PyTorch). probs is a property within the Categorical class of PyTorch's distributions module. float32, torch. My targets are in the form of 1 or zeros, referring to two possible categories. Intro to PyTorch - YouTube Series Mar 1, 2021 · from keras. This code works! y is a 1D NumPy array holding the class number of the samples. 1, 0. Oct 23, 2023 · PyTorch has gained widespread popularity in deep learning for its flexibility and efficiency in handling various data types. Data. You can specify the data type when creating the tensor using the dtype parameter. I have a tabular dataset with a categorical variable defining the batch. Usage Example¶ Aug 9, 2017 · Is there something like “keras. ” — Benjamin Franklin. , with the usage of a label encoder) decreases the quality of the outcome. Jun 7, 2020 · The most common approach to create continuous values from categorical data is nn. The dataset is sparsely labelled, in each image just a small portion of it is labelled, usually with just one class. X. 67 Oct 13, 2020 · Secondly, you might want to extract features from your data, and doing so from categorical data is tough, especially because one hot encoded representations of such data would result in sparse tensors, and applying normal layers of such data would yield in meaningless results. , torchvision. Sep 17, 2024 · Categorical variables are an essential component of many datasets, representing qualitative characteristics rather than numerical values. - EdoardoBotta/Ga Explore and run machine learning code with Kaggle Notebooks | Using data from Categorical Feature Encoding Challenge II PyTorch NN with Embeddings and CatBoost | Kaggle Kaggle uses cookies from Google to deliver and enhance the quality of its services and to analyze traffic. Nov 18, 2019 · I have question regarding the computation made by the Categorical Cross Entropy Loss from Pytorch. This provides a huge convenience and avoids writing boilerplate code. - elyxlz/cdcd-pytorch torch. Can this be achieved using torch. This article explains how to create a one-hot encoding of categorical values using PyTorch library. dtype = torch. I know that to represent str features, I should embed them first. How it works. As Pandas is the most popular way of handling tabular data, this was an obvious choice. Dependent variable Temperature Categorical Variational Auto-encoders in PyTorch. to_categorical(y, num_classes=None) Converts a class vector (integers) to binary class matrix. Unfortunately, I did not find an appropriate solution since Pytorch's CrossEntropyLoss is not what I want and its BCELoss is also not exactly what I need (isn't it?). Dec 21, 2021 · In the demo, we will be using two data sets, A set of image data in which we will build the Convolutional Neural Network and the data in CSV file containing numerical and categorical features with Nov 15, 2019 · There are many ways you can try, but I’ll start with the following. , cat, dog, bird), predicting the next word in a sequence, or making a decision with multiple options. Unlike traditional methods that just convert categories to numbers, this class Oct 13, 2019 · PyTorch is a promising python library for deep learning. Categorical variables perform PyTorch provides excellent support for GPU acceleration and pre-built functions and modules, making it easier to work with embeddings and categorical variables. Intro to PyTorch - YouTube Series Jul 13, 2024 · When converting a DataFrame to a tensor, it is essential to consider the data types of the columns. the connection with the output neuron or neurons depending whether this is a regression, binary or multi-class classification. I’ve seen other articles address this method, but none actually showing how to do it in code. Types of computer memory i. If categorical columns have another data type, they will not be embedded and will be handled like continuous columns. data. L1Loss from PyTorch; nn. Apr 2, 2022 · Hi! I am working on an image classification problem. Each of them has multiple classes. Why PyTorch Doesn't Directly Support One-Hot Vectors. fit_transform(data['C']) #fitting and transforming the desired categorical column. Module): def __init__(self, embedding_dims, hidden_dim, output_dim How to Use a Learned Embedding for Categorical Data; The Challenge With Categorical Data. kl_divergence provides a straightforward way to compute KL divergence Jun 30, 2020 · Hi everyone, I am working on a classification question, where the outcomes contain more than one categorical variable. Categorical. For me reproducibility is important so I set all the random generator seeds to 0 plus whatever was written regarding cublas and deterministic of pytorch… the following steps are done: The seeds are set to 0 at the beginning of the main file. ” Sometimes, the categorical data may have an ordered relationship Apr 7, 2022 · Now I am working with a heavily categorical value based dataset (20 out of 27 variables are categorical) and I have learned I can use nn. and i’m tackling a multi Jul 16, 2018 · Let's take an example with weather data from two different cities: Paris and San Francisco. However, it’s not clear how to define the forward method. Specifically for vision, we have created a package called torchvision, that has data loaders for common datasets such as ImageNet, CIFAR10, MNIST, etc. It seems that simply using concat(A, B) is not a good choice because A, B are totally different kinds of data. I May 21, 2021 · The categorical cross entropy loss function for one data point is where y=1,0 for positive and negative labels, p is the probability for positive class and w1 and w0 are the class weights for positive class and negative class. Embedding to deal with these categorical values. to_categorical” in pytorch. 1, momentum=0. CrossEntropyLoss from PyTorch; nn. An alternative to OneHot for categorical variables in TorchRL. The labels are just 0,1,2,3. Tutorials. However, when I introduce categorical_encoders for my group_ids, the validation loss frustratingly sticks at 36. What I’m trying to do is to create an autoencoder which will encode names of these Apr 10, 2020 · Dear @kumareshbabuns, Dummy variables and embeddings (or word embeddings) are two different things. SGD(lr=. LongTensor. I was pulling the every numerical value for every observation. e. Normally, my model trains well without categorical_encoders, showing a reduction in validation loss from around 9. The additional embedding layers automatically embed all columns with the Pandas category data type. Intro to PyTorch - YouTube Series Sep 11, 2020 · How to encode categorical data that have variable length so could be fetched to nn. A categorical variable is a variable whose values take on the value of labels. Namely, When it is missing, I give it a default null value : x1 = 0, and I have a categorical feature x11 Sep 18, 2024 · How PyTorch Embedding Layer Works (Step-by-Step Guide) “Tell me and I forget. Categorical¶ class torchrl. Sooner or later every data scientist does meet categorical values in one’s dataset. At its core, PyTorch is a mathematical library that allows you to perform efficient computation and automatic differentiation on graph-based models. Milošević and M. g. tensor(categorical_data, dtype=torch. Intro to PyTorch - YouTube Series Apr 26, 2020 · sgd = optimizers. I need to convert the categorical string features to integer ids which can be then used for embedding lookup. Now when I am trying to visualize the effect of embedding for the sex category: every time I get a different vector representation for F. 3 PyTorch version: 1. PyTorch provides two data primitives: torch. datasets and torch. no need to split the data into X and y like in Sci-kit Learn. May 22, 2020 · I have categorical data: sex(M, F) and age (89Y, 55Y, 45Y, 65Y). Names of these categories are quite different - Run PyTorch locally or get started quickly with one of the supported cloud platforms. But at the same time, you expect the weather to change based on the city. In my own words, categorical data is a set of symbols that describe a certain higher level attribute of some object, system or entity of interest. In PyTorch, torch. I want to use CNN for extracting features from images. This is very tedious Apr 6, 2018 · The used function "to_categorical" in Keras is explain as follows: to_categorical. It is often used in machine learning models that deal with categorical data, such as classification and neural networks. Achieving this directly is challenging, although thankfully, […] Oct 31, 2023 · PyTorch tensors serve as the fundamental data structure in PyTorch for data representation and manipulation. I am creating an time-series prediction model using an LSTM, but I also have some categorical information that I want to include in the model. I would like to apply 5 classes. Dataset that allow you to use pre-loaded datasets as well as your own data. Now, I was wondering if I Dec 26, 2019 · For the numerical data, I figured out it was in the data loader/custom data set. Below is my code for LSTM. A few other advantages of using PyTorch are its multi-GPU support and custom data loaders. RAM memory, internal hard disk, external hard disk etc. int64, mask: Optional [Tensor] = None) [source] ¶ A discrete tensor spec. PyTorch Tabular uses Pandas Dataframes as the container which holds data. Intro to PyTorch - YouTube Series May 21, 2020 · My question is regarding the use of autoencoders (in PyTorch). It is as simple to use and learn as Python. Jul 30, 2022 · Hey Everyone, I’m returning to pytorch since a while, and i’m tackling a multi class classification problem for categorical data. oqswmd nemztul qbyjauj uviab qwch guq levoq mxyra vpekyp nky