Questions tagged [binary-classification]

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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>...
Sunit Gautam's user avatar
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2 answers
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Is it vital to do label encoding with target variable

Should I always use label encoding while doing binary classification?
Rus Pylypyuk's user avatar
1 vote
1 answer
244 views

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 ...
User Name's user avatar
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19 views

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 ...
Daniel Wyatt's user avatar
1 vote
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23 views

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 ...
Benjamin B.'s user avatar
1 vote
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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 ...
Gabriel Ballesteros's user avatar
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25 views

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 ...
gotenks's user avatar
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How to customize logistic regression for this case?

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 ...
matttree's user avatar
1 vote
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39 views

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 ...
Michael Mech's user avatar
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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 ...
sensation96's user avatar
1 vote
1 answer
782 views

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: ...
Tollpatsch's user avatar
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332 views

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 ...
Joni Joni -al's user avatar
1 vote
0 answers
52 views

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 ...
simon's user avatar
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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? ...
yoavf's user avatar
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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. ...
Totoro's user avatar
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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 ...
TheNumber23's user avatar
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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 ...
bitebytebit's user avatar
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2 answers
36 views

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: ...
Prabhjot Singh Rai's user avatar
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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, ...
Fiorenzo Fiorenzi's user avatar
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26 views

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 ...
reuben george's user avatar
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24 views

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 ...
Warda_IDRIS's user avatar
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24 views

Decision making in a binary classification problem

Consider a two-dimensional feature space in which the line $\mathbf{w}.\mathbf{x} + b = 0$, where $ \mathbf{w},\mathbf{x} \in \mathbb{R}^ 2 $ and $b \in \mathbb{R}$, separates linearly separable data ...
Tirthankar's user avatar
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Predicting Year-End Outcome from Monthly (and Annual) Data

I have data on customers' usage of various product features over time. Each month, a customer can choose to use a feature or not. I want to create a live system that produces the probability of a user ...
pyassign67's user avatar
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81 views

Binary classification metrics for one-hot label encoding in Tensorflow

I run a binary classification using different CNN versions in Tensorflow. When I label samples from each class using 0 and 1, I select a sigmoid output in the last layer of the CNN, like ...
GKH's user avatar
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BCE loss stuck at 0.693 in the beginnng of training and then started to decrease, why?

I'm using a Transformer encoder with a binary cross entropy loss for CTR prediction. The training batch loss is at around 0.693 constantly for the beginning several thousand steps (batches). I'm using ...
CyberPlayerOne's user avatar
0 votes
1 answer
12 views

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 ...
secuf's user avatar
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Comparing probability models for alignment

I have a probability model which predicts a probability for a binary classification problem. I am interested in how well the predicted probability aligns with the true probability. For instance, you ...
Moss Richardson's user avatar
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190 views

Weighting and loss function for multi-dimensional output on ECG neural network in Tensorflow

I am working on a DNN that is training on ecg data with a shape of [None,1,2500] and output shape of [None,12,19] where 19 is a ...
ekg-display's user avatar
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1 answer
119 views

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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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 ...
C.S.'s user avatar
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38 views

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 (...
makala's user avatar
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0 answers
271 views

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 <...
Farzad's user avatar
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1 answer
59 views

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 ...
Rory Majule's user avatar
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0 answers
16 views

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 ...
Stak33's user avatar
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0 answers
36 views

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 ...
SaulGoodman's user avatar
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0 answers
396 views

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 ...
Haider's user avatar
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1 answer
27 views

Understanding Classification model results

In a certain binary classifcation problem I am getting a AUC of 1 and Accuracy,FI,Recall,Precision of ~99.7 both in train,test and holdout sets. But when I run the model on unlabelled data which I ...
Scope's user avatar
  • 101
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0 answers
22 views

Sampling Highly Imbalanced Large Dataset

I am working on a model which will run monthly on 8M users. I've snapshot-wise data in training set, eg: Jan, 21 Snapshot : 8M Total : 233 Positives Rest Negative Feb, 21 Snapshot : 8M Total : 599 ...
Harshit Gupta's user avatar
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1 answer
52 views

Give more weight to features based on distribution plot

I have a task to predict a binary variable purchase, their dataset is strongly imbalanced (10:100) and the models I have tried so far (mostly ensemble) fail. In ...
robsanna's user avatar
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1 answer
387 views

Which classification_report metrics are appropriate to report/interpret for a binary label? Individual or macro average for both classes? scikit-learn

First, please forgive my ignorance; I am a newbie but dedicated to learning more. Example: I have a using a random forest classifier to predict a binary outcome. The binary outcome equals 1 if people ...
iamtheonewhoknocks's user avatar
0 votes
2 answers
123 views

How to deal with temporal trend in ML

I am fitting a binary classifier and I observe a temporal trend in the response variable, meaning that the actual percentage of positives fluctuates with time, I can see periods where it is high and ...
Anatole's user avatar
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0 answers
136 views

Handling Overfitting(diverging val loss and train loss) in Conv-LSTM

This is related to an Educational Research Project I'm using to learn stuff so please be kind. I'm working on a video binary classification task. I have almost 1400 videos of both classes. I also ...
Hashim's user avatar
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For a binary classification algorithm, is there an objective way to know how large your set of positive and negative labels need to be?

We're training a binary classification algorithm using a combined total of 2000 positive and negative labels that we purchased from a data vendor. We mostly used all the textbook machine learning ...
Codo Baggins's user avatar
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0 answers
60 views

How to prepere dataset for binary classification (anomaly detection?) on timestamped sensor data (multiple files)?

my goal is to make prediction (good or bad data) on sensor data. I tried a lot, but failed to shape my data to get the desired output. scenario: I have multiple timestamped (time as it self is not ...
low's user avatar
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0 answers
222 views

TSNE interpreration and separability

I have a binary classification problem where I train a neural network on a training and validation data sets. But I am not satisfied with the performance of my trained classifier (the NN above). The <...
Imaxd's user avatar
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0 answers
14 views

Non-Classification outputs in a classification problem

maybe this is a very stupid question, so please excuse me as I am a total beginner in Machine learning. I have a dataset divided into X (shape: 10000, 599), Y(shape: 10000,). Y is simply zero or one. ...
Riddhiman Raut's user avatar
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0 answers
35 views

Should I balance the test set for predicting probabilities in binary classification?

In my dataset I have 75 % of "0" class instances and 25 % of "1" class. It a real world ratio between classess. I balanced my training set, and trained the model. Then, depending ...
mad_scientist's user avatar