Questions tagged [binary-classification]
The binary-classification tag has no usage guidance.
66
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Obtaining threshold based rules for classification problem
Suppose there are X1...Xn numerical variables predicting a target variable Y (0 or 1)
Objective: to obtain the best possible thresholds and combinations of X1...Xn that can predict Y
Example: (X1>...
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How to make logistic regression give some observations more importance/weight?
I have a binary classification problem, with a dataset comprising of several features. When I train LogisticRegression on it, I get large number of false positives ...
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Class imbalance for binary classification tasks
I am looking to train a binary classifier. Most of my experience so far has been with generative models, not classifiers, so I am wondering with respect to training data, what is a good ratio of 0 and ...
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Interpreting the SHAP values presented in layered violin plot in SHAP-library for Scikit binary RandomForestClassifier
I am using the SHAP-library for computing feature Shapley values for a binary RandomForestClassifier which has naturally two outputs, 0 or 1. The forest itself consists from 100 decision tree ...
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About GANs specialization
I'm working in a binary classification problem. In general terms, I'm focused on using neural networks to classify datasets that are not huge and in order to address that problem I'm comparing ...
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When can we claim that the training converged?
I've been working for a while in a binary classification problem with different types of neural networks. In this particular case, I'm using an 3-layer MLP with hyperbolic tangent activation in input ...
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1
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792
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precision and recall is zero
Why my model shows metrics like this? While my model was training recall and precision was equal to zero?
I trying to do binary classification of mushrooms [edible, poisonous]. I have CNN model with ...
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How to calculate threshold values for a simple binary classification model
Consider a binary classification problem with two features. Let's assume that the higher the value of each feature the more likely a datapoint is to be positively classified.
Additionally assume we ...
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Data filtering framework
I have procurement data that needs to be labeled with product categories. It's tabular data, containing 700k rows and a mix of data types (dates, free text, floats, etc.) The product set we currently ...
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Best way to represent a version feature based on percentiles
We're training a binary classifier in AutoML, and one of the features consist of browser versions.
Currently these versions are provided "normalized" to the model, according to the ...
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Repeating values caught with a binary classifier
If my machine is broken, it starts to repeat certain channels. Thing is if there are no out-liars, it is difficult to tell it's broken as we would expect all data points to be around the same value. I ...
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Fractional Differencing/Differentiation for Non-Time based Model; Look-ahead bias?
I have time-series data, but instead of using a time-based model like RNN, I've decided to approach my classification problem using an lgbm classifier. To do so, I have modified the data, such that ...
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Suggestions for binary time-series-classification model for small dataset
Hopefully I´m at the right place for my question:
I´m looking for suggestions for models to use to classify multivariate time series. I´m trying to find a way of classifying the behaviour of motors ...
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Finding logistic loss/negative log likelihood - binary logistic regression classification
I am new to ML and data science and am struggling with a simple problem. In my problem, I am given a series of datapoints $X_i$ where $X_i = (x_{i1}, x_{i2})$ with each data point having a label $y_i$ ...
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Loss drops to NaN after a short time for a time series classification
here is my model code for a binary classification of a time series:
...
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How to pass manually split data to cross-validation
I have to perform a binary classification. My dataset is quite small 280 samples and quite imbalanced (1:10 ratio). I kept around 100 sample as testing and about 140 for training. My input variables ...
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Testing a Binary Classifier
I have been training a binary multilayer perceptron on a database made out of roughly 3600 0 values, and 4 1 values. Afterwards, I'm testing the MLP on a test set made out of 7 0 values and 7 1 ...
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Classification problem with 2 level features
Consider an automated house, where we can collect router data every minute. The objective is to predict router fail/no fail (1/0) in a future time window.
The router sends data at two different levels ...
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XGBoost Binary Classification for authentication hyperparameter tuning
Im currently working on biometric authentication, specifically on keystroke dynamics. I used XGBoost as binary classification so i can calculate equal error rate (EER) and ROC-AUC. Now i want to do ...
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Expecting your comments to structure my project?
I am developing an binary classification project. Initially I got a dataset including real data in 3290 rows and 15 columns. Then using CTGAN network I generated synthetic dataset with 100000 rows. ...
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1
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How to tune the classification threshold in a cost-sensitive manner?
I have trained a classifier outputting probabilities for each class. I want to tune the decision threshold in such a way that it accounts for different costs/gains assigned to false positives ($FP$), $...
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Why PySpark `BinaryClasssificationEvaluator` metric `areaUnderROC` returns slightly different across multiple evaluations on the same dataset?
I am using BinaryClasssificationEvaluator in Pyspark to calculate AUC, however, I find that the returned auc across multiple evaluations on the same dataset are ...
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Remove feature from kernel matrix
I am pretty new to machine learning so please bear with me :)
I am trying to do a binary classification task using an SVM with precomuted kernel (in python using sklearn).
I created my train kernel ...
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Getting nearly 100% accuracy using Binary Classification in Tensorflow but incredibly wrong prediction levels for email messages
I'm creating a Chrome Extension to read user emails via Gmail's API, and then passing in user emails to a trained Keras model in Flask to determine whether the email was written by an AI or a Human, ...
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How can i retrieve incorrect labelled predictions on new unseen data on image classification when i want to retrain my model later on?
I've recently made a binary image classification model with transfer learning. The model is used on an api and the predictions gets saved into a database. The problem is that it predicts images ...
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Multilabel Classification - Flat Binary Classifiers vs Hierarchical Binary Classifiers
Was researching on multi label classification to solve the problem of tagging news articles with topics and countries, where tags follow the syntax <topic>-<country>, and would like to ...
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How does hyperparameter tuning work for constructing/choosing a final model using Nested Cross validation?
I want to determine if XGBoost is better than random forest or logistic regression for building a binary classification model. The model will be a composite model, with a feature selection model to ...
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13
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Computing empirical coverage in binary classification confidence estimation
I am trying to compute confidence interval empirical coverage. I have computed the confidence intervals for binary probability predictions. For example, the model estimates that there is probability ...
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Are my CNN loss and performance curves valid, or are they showing under or overfitting?
Thanks in advance for any help offered.
I am using a Keras CNN to perform binary classification (credit card transactions fraud vs non-fraud). Below is my results for 100 epochs.
It feels odd that the ...
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39
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While working on binary image classification, the class mode set to binary incorrectly labels the images, but does it correct on categorical
I am currently working on a binary image classification. My problem is that when i use data augmentation, it incorrectly labels the images when it is set to binary.
The things i have tried:
Looked ...
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66
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Can someone interpret my Binary Cross Entropy Loss Curve?
I am trying to understand my loss curve using : tf.keras.losses.BinaryCrossentropy()
Question 1: Based on my loss curve/accuracy, would it be wise to proceed to feed it into a ensemble learning model ...
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Binary classification penalizing only one class
Let's suppose that the task is the following binary classification: Does this image have a cat or a dog? These are the four possible predictions:
It predicts cat and the image has cat.
It predicts ...
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35
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Gradient boosting algorithm implemented in LightGBM
I'm currently reading the documentation of LightGBM and I'm wondering which gradient boosting algorithm is exactly implemented there if choose boosting parameter as "gbdt" or "dart?&...
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Pycaret cross validation scores are way lower than unseen test set scores
How come the cross-validation model scores are much lower than the model scores on an unseen dataset?
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173
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Empty Confusion Matrix and Zero Precision/F-score
Could you please say why I'm getting this warning while doing a binary classification using Artificial Neural Networks? The data are colored images.
...
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Model can not fit to 8 datapoints
I have 10 groups of biological experiments, all of size 100. I want to estimate experimental performance (success rate) of each groups of experiments, but have only ran experiments in two groups. My ...
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Why is my Histogram Gradient Boosting Classifier model still producing type II error? How can I reduce the type II error?
Type 2 error and how to hypertune or feature engineer a solution for it
I trial and tested different techniques and kept the structure which made the most sense to me. But still my model confusion ...
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Binary classification using RNN not going beyond 50% accuracy
I am trying to find out the reason behind why my RNN network won't go beyond 50% for binary classification. My input data is of the shape:
...
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Competition test set performance much lower than validation set
We are a team of 3 participating in a university competition for a deep learning course. The competition involves a binary image classification task where we have to predict leaf diseases on a (5200, ...
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Logistic regression with E-net regularization produces different set of weights with each run
I am currently trying to make a model to classify brain tumor patients by incidence of epilepsy using a combination of variables extracted from clinical records, and radiomics features from segmented ...
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27
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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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1
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Classification Problematics : Feature Number Variance & Feature Repetition
I have a harsh case study (in my mind).
The problem is I need make binary classification on Quality of Service (good or bad). I have a feedback on quality on groups of devices belonging to company. I ...
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Decision boundary of an neural network
Starting with a).
For the first unit: 0 * x1 + 1 * x2 + 1 > 0 (0, because the threshold is 0) which is the same as x2+1 > 0.
For the second unit: x1 * 1 + x2 * 0 + 1 > 0 (0, because the ...
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246
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Classifier calibration leading to worse outcome
I am trying to calibrate some classifiers to output more accurate probabilities. For this, I am using a sigmoid regression as implemented in ...
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Binary Classification: My model classfies most data (95%+) as label 1
I am working with ECGs and trying to use a CNN model to perform binary classification. The goal is to classify 30s ECGs to detect a specific disease. I am using CNN and converting ECGs to images (...
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318
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Custom loss function for binary classificatio in Keras gets error: No gradients provided for any variable
I have a binary classification problem. However, I don't really care about fp and fn values. What I want to achieve is that the <...
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Input Tensor Shape for CNN Binary Classification of Time Series Data
I want to predict whether a machine will fail based on the most recent set of measurements taken by on-board sensors. I have several dozen machines, each with a sensor that takes a measurement at ...
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ANN Input vector representing a network of contests's outcomes between thousands of individuals?
Let's say I have 1000 participants within a tournament, each of the participants having a single ID. One instance of this tournament is a 1 vs 1 contest when one wins and the other loses.
Now let's ...
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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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533
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Classification report for binary class problem
I am building a binary CNN using single neuron in the last dense layer and "binary_crossentropy" as the loss function while compiling to predict either class1 or class2. I am having problem ...
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