Questions tagged [binary-classification]
The binary-classification tag has no usage guidance.
129
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"Inverted" correlation of the amount of the loan and the probability of default
My task is to make a machine learning model for predicting the probability of default when taking a loan (binary classification).
But I also want to predict the loan amount. Something like: if the ...
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Good models for predicting whether a customer would make a purchase given details like age, gender, ethnicity, salary, etc?
I have around 30,000 data points and for those data points I have some numerical fields like customer_age, ...
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92
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CNN model for binary classification
I just finished training a CNN model for binary classification. Two diagrams were created afterward, but as I am very new to machine learning, I don't get what they state. can anyone tell me if my ...
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691
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How can SHAP feature importance be greater than 1 for a binary classification problem?
Let's say I build a binary classification model to predict survival on the Titanic. I then use SHAP to get feature importance for each feature. I see that the SHAP importance for the top feature, <...
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113
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Binary classification performance difference between 0 and 1 class
I have trained a binary Random Forest classifier on a dataset containing 7M rows. I also set aside a holdout validation set of 1M rows that the training pipeline never sees. The dataset consists of ...
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Machine learning algorithms for tabular dataset
I have a dataset with 120 features and 5000 instances. The dataset is combination of categorical and numerical values. It is a tabular dataset. My problem is a binary classification problem. I trained ...
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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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33
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How to predict the quality (as a classification) of a regression?
For an industrial workflow we created an ANN (in tensorflow) for a regression problem where we take customer inputs (as numeric values) and predict the measurements for the units that need to be ...
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181
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Timing of applying random oversampling on the dataset
I tried to learn classification using machine learning algorithms. I went through Breast Cancer - EDA, Balancing and ML the notebook. In this notebook ...
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1
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153
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Finding research papers for a dataset
I found a breast cancer dataset on Kaggle. Here is the link - https://www.kaggle.com/datasets/reihanenamdari/breast-cancer
I would like to how could I find out which research papers use this dataset ...
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138
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Different result of classification with same classifier and same input parameters
I did a binary classification using "Random Forest".
The code block is
...
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265
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Select threshold (cut-off point )for binary classification by desired fpr persentage value
I want to recreate catboost.utils.select_threshold(desc) method for CalibratedClassifierCV model.
In Catboost I can select ...
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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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Validate Unsupervised Binary Classification
I’m working on a fully unsupervised anomaly detection problem. Since it’s completely unsupervised, I’m having hard times in defining some metrics to kind of validate the results (I run several ...
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ML/DL model needed to perform binary classification on binary input image dataset
I desperately need help regarding ML/NN models that would be appropriate for binary input data..
So, I have an image dataset in which [R,G,B] values can only take ...
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319
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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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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 ...
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Method of accuracy improve for binary classification imbalanced dataset
I have an imbalance data set where the imbalance ratio No: Yes is 8:1. If I run classifiers on the groundtruth dataset I got recall and F2 measure for Naive bayes, Logistic regression, random forest. ...
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The best Python library to build decision tree on binary inputs
Please, could you advise me the best Python library for the following problem.
I have 60 binary input variables and a binary output variable. There are 10 000 – 20 000 training examples. I want to ...
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Binary Classification with Very Small Dataset (<40 samples)
I'm trying to perform binary classification on a very small dataset, consisting of 3 negative samples and 36 positive samples. I've been testing different models from scikit-learn (logistic regression,...
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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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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 ...
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360
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Keras Binary Classification - Maximizing Recall
Let me start by saying my machine learning experience is... dangerous at this stage. I'm still a beginner.
I have a binary classification data set of about 100 000 records. 10% of the records are ...
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362
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How to use confidence labels?
I have 2 sets of training data in csv files. The training data have class labels, 1 for memorable, and 0 for not memorable. In addition, there is also a confidence label for each sample. The class ...
user135529
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110
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Meaningfully compare target vs observed TPR & FPR
Suppose I have a binary classifier $f$ which acts on an input $x$. Given a threshold $t$, the predicted binary output is defined as:
$$
\widehat{y} = \begin{cases}
1, & f(x) \geq t \\
0, &...
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370
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ROC-AUC Imbalanced Data Score Interpretation
I have a binary response variable (label) 𝐵 in a dataset with around 50,000 observations.
The training set is somewhat imbalanced with, 𝐵𝑖=1 making up about 33% of the observation's and 𝐵𝑖=0 ...
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Is it vital to do label encoding with target variable
Should I always use label encoding while doing binary classification?
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61
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how to interpret precsion recall value in binary classification of scikit-learn
I am working with binary classification and my classification report generated through scikit-learn looks like the image below. I am confused I have two precision-recall values one for class 0 and the ...
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419
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Improving roc auc score when accuracy is good
I have got a binary classification problem with large dataset of dimensions (1155918, 55)
Also dataset is fairly balanced of 67% Class 0 , 33% Class 1.
I am getting test accuracy of 73% in test set ...
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neural network binary classification softmax logsofmax and loss function
I am building a binary classification where the class I want to predict is present only <2% of times. I am using pytorch
The last layer could be logosftmax or <...
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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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45
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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 ...
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327
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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 ...
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161
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Separating numerical and categorical features in a binary classification problem
I have a dataset with employee data with around 9500 rows, and have to predict if the target is 0 or 1.
Some of my features are the department of an employee, gender, salary, review_score(numerical),...
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Where shall I use odds logarithm and when shall I use sigmoid in logistic regression?
I have been interested in DS and ML recently and logistic regression was on of the first algorithms I learned. In my first course it was said that ln(p/(1-p) was used for the logistic regression. But ...
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Creating Dataset for Classification, How much balanced a good dataset should be?
I am creating a dataset with 4 classes, and there are 50K rows, I am already getting 86% accuracy, 0.85 Precison, 0.86 Recall and 0.71 F1-Score on SVM with 80,20 split.
I have to publish this dataset ...
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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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100
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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 ...
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199
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Binary classification with seperate training and testing datasets [closed]
I have two datasets (train.csv) and (test.csv) revolving around predicting the death outcome for a disease. Both sets include 20 independent variables (age, weight, etc), but only the train.csv ...
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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 ...
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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 ...
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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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Is binary classification the right choice in this case?
I am somewhat new to text classification and I have some questions if you folks can help:
I have some text I need to be able to classify as belonging to a single class or not (usually 1-10 sentences ...
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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 ...
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Using precision as a metric - how to gauge if more TP's
So precision is calculated as tp/(tp+fp)
But this doesn't seem to be a good way to assess a model as both of the below would give a result of 1?
Binary Classification
...
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Which machine learning algorithms are more suitable for binary classification?
We know that there are many different types of classification algorithms. But among the different categories of classification algorithms, which algorithms are suitable for binary classification and ...
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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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632
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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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Binary Classifier , when Data Points are very less and number of features are very large [closed]
I am building a Binary Classifier.
There is no Real World Scenario Problem Statement, We have just given only the data set and some guidelines.
Number of features : 2040
All features are in decimal ...
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3
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What could go wrong if I sample before classification?
I have a million entries in a table that I can use to train a binary classifier. Only 30 thousand of them are positive. Is there anything fundamentally wrong with selecting around 30 thousand negative ...
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