Questions tagged [classification]

An instance of supervised learning that identifies the category or categories which a new instance of dataset belongs.

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Using LSTM for binary text Classification, getting almost same accuracy at each epoch

I am doing Twitter sentiment classification. For that I am using LSTM with pretrained 50d GloVe word embeddings(not training them as of now, might do in future). The tweets are of variable lengths ...
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21 views

Checking if ML model is possible

How can I check if a machine learning model is feasible on a given dataset? What techniques like EDA, correlation etc. can be used to judge if a model is possible i.e. data and predictor variables ...
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The effect of imbalanced distribution of data

I read on Google's ML website if I have classification dataset with a ratio of 90% for one classification and 10% of the data for another classification. In that case, should I use the exact same ...
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Nan in target variables Neural Network

Is it possible to train on a dataset with some nan in the target variables? I imagine a sort of loss calculation only for the given target data. Is this Doable in Tensorflow/Keras =?
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23 views

Binary classification algorithm

I have a retail customer transactional data set with features such as customer ID, product, date, number of products bought, ZIP code of customers, amount of the transaction. There already exists a ...
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True positives and true negatives, F1 score: multi class classification

I have 4 classes for an application of classification of animal kingdom: 1 --> invertibrates; 2 --> vertibrates; 3--> mammal; 4 ---> ambhibian. Given a mixture of images the objective is to identify ...
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1answer
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How to train a Machine Learning model for blocked data

I'm concerned with a supervised classification problem for the following type of data. The data consists of $N$ rows (where $N$ is not very large - this is not a big-data problem) and $M$ columns (...
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1answer
13 views

How to approach this multi label classification problem and what will be its accuracy metric?

I have a dataset for people doing trade in various segments (classes) .I am trying to build a multi-label classifier to predict people trading in various segments (classes). My dataset : ...
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1answer
28 views

Which Technique should we use for predicting an integer output?

I'm working on a problem where my target feature of type integer. i.e (n_clicks). In general, if we want to predict categorical target feature then we use classification algorithms and on the other ...
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How to justify the usage of 200 dimensions in word vectors instead of the 300 dimensions?

When employing machine learning methods in NLP, most of studies use 200 or 300 dimensional vectors. 300 dimensional embeddings carry more information and this, therefore, is considered to produce ...
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Should scaling be done for mixed data (categorical and numerical)?

My dataset contains 13 attributes consisting of 10 Numerical and 3 Categorical attributes and Target. It has 180 observations ...
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18 views

How to handle a data set with large number (about 75%) of binary variables?

I am doing a research right now and want to classify (predict) churns of costumers using machine learning. My data set consists of about 500,000 observations with 20 variables: 15 are binary, 2 ...
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Scikit learn - best model to classify supervised two-feature data?

I'm quite new to scikit learn but I am looking for the best approach to go about classifying some data I've collected where each set contains two measurements made over several points of time, along ...
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Classify the input set into categories based on pre-defined rule set

I'm trying to solve a problem where there are 2 input files given, Input 1: A set of strings in the format. All the strings start with "A". A-B-C-D A-B-C-3 A-X-Y-Z-4 A-X-5 A-X-P-Q-R A-X-P-Q-S A-M-9-...
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Understanding python XGBoost model dump output of a very simple tree

I am trying to understand the model dump output from XGBoost. I would like to step through and see exactly how the model arrived at it's prediction. To simplify I trained a model with 1 tree and 1 max ...
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2answers
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Improving classifcation when some are less represented?

I have a multi-class classification problem. It performs quite well but on the least represented classes it doesn't. Indeed, here is the distribution : And here are the classification results (I took ...
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Big dataset for multi-class classification can't be dasked and split, normal one can't be handled

I have a huge dataframe (550MB), the lending club one available here, and I have to predict the class of the grades. The dask dataframe is : ...
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Keras ANN Trained Model's Accuracy change on prediction

I have trained an ANN Binary classifier using Keras. It gives 90% accuracy. After testing when I predict same data again but pass only one class then accuracy decreases to 40%. I have figured out ...
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Transforming target from object array to integer array to use sparse_categorical_crossentropy for class prediction

I want to do a neural network to predict to which loan class does a borower pertains. There are 6 classes [ A, B, C, D, E, F]. I tried to get rid of the NAs and ...
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How to stack classifiers optimized for different score functions?

I have binary classification task (class0 vs class1) and I would like to create a stacked model out of classifiers which are individually optimized for different scorings. For example, let's say Clf_A ...
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Keras model with second to last sigmoid activated Conv1D layer followed by globalMaxPool outputs values outside [0,1]. Why?

I am trying to train a binary classifier. It is a residual network with skip layers etc. but ultimately, the bottom two layers are a 1D convolution with sigmoid activation followed by a global max ...
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How to check if a class segmentation is meaninfull?

Using the Lending Club dataset I have a data frame with the loan characteristics of some borrowers. Here is the distribution of the subgrades: ...
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1answer
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Churn Prediction Training Set

I don't understand how to form my dataset from activity(logins etc.) and characteristic(location, age etc.) raw user data. Ultimately, each row of the training set will have N activity features for a ...
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Binary classfication vs One-class classification

Why do we need samples of both classes for the training of binary classification algorithms, if one-class algorithms can do the job with only samples from one class? I know that one-class algorithms (...
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Use LightGBM or FFM - imbalanced dataset

I have a highly imabalanced dataset but one that is not sparse. In train there are 1328 positives out of 104000. In validation ...
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BinaryRelevence Classifier giving errors during predicting accuracy scores

I am new to MultiLabel Classification. I have a data set of audio acoustic features and I apply Binary Relevance Algorithm. The output works fine. But when I start calculating the accuracy scores I ...
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When should I prefer XGBoost over others?

Given all the other bagging and boosting algorithms, when should I prefer XGBoost over others?
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Performance of Triplet loss network vs multiclass classification

I am training a triplet loss based classification network and a normal multiclass classification network on some image data. In my case, triplet loss network performs poor than multiclass network. I ...
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How to represent audio data in a format that can be used for preprocessing and modelling?

I have a project that I am working on currently. The project is to classify audio data. The data is in two folders train and test...
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1answer
85 views

Feature Importance

I have a dataset with 10 features. I've computed the feature importance using permutation importance with cross-validation from eli5, after fitting an extremely randomized trees (ET) classifier form ...
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Loss and accuracy remains constant in time series classification by LSTM

I have a time series data with a classification label of 1 and 0. I am using a LSTM model to classify the series by taking 100 consecutive timestamps as input with a single label. Even after training ...
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1answer
11 views

adding non-failure data to failure one

I have a dataset containing features of different engines showing when they failed. I want to build supervise learning model to predict whether an engine with a certain mileage is going to fail or not....
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Is it possible to build an intelligent lead classifier with just a few training units

I want to build a lead classifier for my Master Thesis and wanted to ask for an assessment of feasibility. Here are the key points: (1) We have 15 customers and about 100 opportunities of which we ...
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37 views

Naive Bayes for Categorical Features (Non Binary)

How do i use Naive Bayes Classifier (Using sklearn) for a Dataset considering that my feature set is categorical, ie more than 2 categories per feature are present. I've looked everywhere, some ...
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What is the accuracy majority class classifier?

I have an SFrame and a model: ...
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How to implement Classification and Anomaly detection (C++)

I am creating a system using C++(DX11) and i'm reading raw data into my program, i want to classify what the 3D data-set i'm reading in is and detect any anomalies it may have when compared to a ...
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How do classification algorithms assign coefficients or importance to a categorical feature

I have a binary classification dataset with target variable being response (Y/N) and one of the predictor variable is month. Image has data on respondents count and rate by month (sorted by ...
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60 views

Can I arbitrarily eliminate 20% of my training data if doing so significantly improves model accuracy?

My dataset contains 2000 records with 125 meaningful fields 5 of which are distributed along highly skewed lognormal behavior. I've found that if I eliminate all records below some threshold of this ...
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How can I train a machine learning model with below characterstics? [closed]

Hi I have a classifier model to solve, which has close to 56k samples and 30 features which ...
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13 views

How to create Independent Neural Networks by changing the architecture?

I am trying to build 3 independent Neural Networks that are trained on the same dataset and fuse the NNs using a voting system. I came to the conclusion that the effectiveness of the voting system is ...
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How to solve a classification problem with multiple time series?

I am trying to build a model for credit default prediction. I've got a dataset of over 20,000 customers and the features are their payments over the last ≤24 months. The dataset looks like this: <...
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1answer
32 views

What do we learn from training a dataset for logistic regression

What do we learn from training our dataset in Logistic Resgression? Like in Linear Regression, with the help of training set we are able to generate a best fit line(y = mx+c) where m and c come from ...
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Handling hierarchical category independent variables

I have data with huge categorical attributes. For example, main_column, sub_column1, sub_column2 are 3 hierarchical attributes. If if take dummy variable on these columns the column count is ...
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81 views

Why do we use the F1 score instead of mutual information?

We often use the classification threshold that maximizes the F1 score, if we don't have a prior cost function of false positives and false negatives. This balances the desire for precision and recall....
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Has this paper used weighted KNN or not?

Please tell me if you see this paper in the link below has used weighted KNN? because they have used weights as the training and testing samples and no formula written. They don't explain the ...
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Does it make sense to use Transformer encoders on top of a pretrained Word2Vec embedding for a classification task?

As the title says. I am dealing with a text classification task, but I do not have the resources to train a BERT word embedding from scratch. I was thinking of using an existing Word2Vec embedding ...
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XGBoost predicting everything as null when sample weights are passed

I am trying to build an Uplift model using observational data. The data is consists of collections calls to customers and my objective is to predict the incremental probability due to the treatment (...
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Day Classification in Time Series - LSTM

I am working on a problem in which i have a daily time series and I have a label for each day. For simplicity, let's say it is a binary classification, so for each day, there's a label (0 or 1) and 1 ...
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51 views

What does high variance mean in a binary classification machine learning model?

My understanding of high variance is that the targets are spread widely around. The output values are "all over the place". In a binary classification model, there can only be 2 outcomes. I am at a ...