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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24 views

how can i compare two classification algorithms?

all, i have two classifiers (xgboost and light gradient boosting) to predict if yes cancer or not. when i use roc_auc as my scoring method i get xgboost as 0.75 and light gradient boosting as 0.76. ...
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Classification Algorithm for Segmented Regression Time Series

I have Segmented Linear Regression dataset in JSON format like this: ...
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1answer
17 views

ANN Classifier for extracted discrete image features

I have a features extraction algorithm that works well to extract features from images. I want to develop an ANN to classify those images based on those features. I have extracted features in a csv ...
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Machine learning goal: given a population of 100,000 students, predict a group of 3,000, and minimize the median grade of that group

In other words, I am looking to predict students that will fail out of school before it happens. The data includes socioeconomic status and other related variables. I have tried an XGB binary ...
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Should the baseline comparison be done on the training or the test set results?

I have a classification problem where I want to find out whether feature engineering has improved my final model. Cross-validation is used evaluate the impact of the feature engineering steps, so ...
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Cross validation schema for imbalanced dataset

Based on a previous post, I understand the need to ensure that the validation folds during the CV process have the same imbalanced distribution as the original dataset when training a binary ...
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How to identify possible false negatives in labels?

I am working on a drilling difficulty prediction project using machine learning method. The dataset has lots of geological data as the main features. It also has ...
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1answer
25 views

how to classify text based on more than one column

I passages of text to classify by topic. I am using scikit learn, e.g. linear svc, but open to other options. Currently, use only the text of each passage (column labeled ...
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How do I build a model to improve CTR on campaign?

I am trying to build a propensity model for a client to increase the CTR. Client has the list of people who clicked in the previous campaigns but doesn't have the data on the list of people who didn't ...
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Student Churn prediction

I am working on an ML model for student churn prediction. It is a classification problem if some student will churn or not. I have a lot of data like the student data and the activities of the student....
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Simple Binary Classification Example in Python

I'm not sure the correct place to ask, but I'm trying to develop a simple function/algorithm that outputs a predicted number from a sequence of numbers (I have a background in Python, but little to no ...
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60 views

For imbalanced classification, should the validation dataset be balanced?

I am building a binary classification model for imbalanced data (e.g., 90% Pos class vs 10% Neg Class). I already balanced my training dataset to reflect a a 50/50 class split, while my holdout (...
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28 views

XGBoost Log Loss different from GridSearchCV Log Loss

I have a classification problem where i am trying to predict if the data returns a 1 or 0. So you're classic binary classification. I have my set of data that I have split into the dependent variables ...
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What to input into machine learning algorithm for image recogniton?

I am working on a project that involves classifying images as either that of a cat or that of a dog, without using CNNs. I used SKImage to convert the images to a matrices and changed it to grayscale ...
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How to get the probability/closeness of a sample belonging to a specific cluster?

I'm new to this so please let me know if my logic of comparing cosine similarity and k-means is incorrect I got a set of ...
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Chossing between Prediction vs classification model for dataset having daily record(date value)

I have a use case, where I have 4 classes based on the score, for example class 1 : when the score < 10 class 2 : when the score between 11 to 20 class 3 : when the score between 21 to 30 class 4 ...
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ValueError: Found input variables with inconsistent numbers of samples [duplicate]

I am trying to do svm model training and it gives this error: ValueError: Found input variables with inconsistent numbers of samples: [91, 212] Code: ...
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Lower-than-random ROC

If I have an ROC for a single classifier [y(x) in the range 0...1] that is 'worse than random', namely the AUC of the ROC is less than 0.5, would a classifier that reversed the class predictions [y'(...
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What should you do with attributes that predictive in an interaction?

I am trying to predict results of football games. Some of our attributes only give meaning for a prediction only when they are considered in interaction with another attribute. To illustrate, a team ...
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Classification model to classify large number of classifiers?

Hello I am very new into the field of machine learning/deep learning , and I am finding it hard to select the right model for my research. What I am trying to build is a model to classify which ...
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Random Forest vs Neural Networks

Why neural network is considered a better model than random forest in a classification task ? I've tried both the models in a 10 class classification problem and the random forest perform better than ...
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How to convert images (.jpg) to vectors for image classification

I'm currently working on a project that involves classifying an image as either that of a dog or that of a cat. The twist is that I want to do this without using Convolutional Neural Networks, mainly ...
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Input with variable length Classification problem

I have a dataset with patient information with discrete labels (labels are stages of a particular disease) which needs to be predicted (Basically a classification problem). The dataset looks ...
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1answer
35 views

Finding a range of values for each feature that contribute to positive classification

Consider a classification problem(lets say 2 classes, 'good' and 'bad), where all features are continuous. what I need is a range of values for each feature that contributes to 'good' classification....
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Handling multiple data types in Naive Base

I was studying about NB Classifier and it came out to me that i can use Bernoulli NB or Multinomial NB for categorical variables ...
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Machine Learning - Input Prepocessing - NLP email classification model

So I created a model which classifies emails into different categories, just like a spam filter. I deployed the model as a webservice, no problem with that but I can’t get my head around how I would ...
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ML: Classification Model Comparison

Given is a dataset that I need to use for a classification and I want to compare the performance of different classification models. Let's assume, I want to look at logistic regression (with ...
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What would be my features?

I was thinking of training a neural network that would be able to classify twitter users according to their followers. For example, I would like to know if a user is "gamer" or not by the people he ...
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What is a semi-supervised learning model capable of detecting new classes?

Suppose I initially want to distinguish between dogs and cats based on various numeric features (tail length, weight, etc). I have some labeled data for both classes, but also a large amount of ...
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How to grid search class.weights hyper parameter in Ranger?

I am currently using ranger for binary classification. My dataset is highly imbalanced (10:1). I went over the documentation, and it appeared to me that ...
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Gaussian Process Classifier and specifying kernel

I am using scikitlearn's gaussian process classifier and either I don't think I understand how the kernel is used (more likely), or there is an error in the module (less likely). In short, the ...
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1answer
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How much data should I allocate for my training and and test sets? (in R)

I have a matrix of 358.367 data. Each row is a DNA sequence from the human genome. I want to build a classification model in R, using XGBoost algorithm and 83 features (dinucleotides, trinucleotides, ...
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29 views

Info obtained from a confusion matrix

I am new to data science and I am trying to understand the use/importance of accuracy, precision, recall, sensitivity and f1-score when I have a confusion matrix. I know how to compute all of them ...
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H2o Model Classification - Confusion Matrix Vs AUC

I am running a Classification Model in Python using H2o When I checked the model Performance on test dataset I got ...
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3answers
53 views

Why would it be bad to fit a linear regression to a binary classification problem?

Let's say that we have a binary classification problem. Why would it be bad to fit a linear regression and then classify given a threshold? The output would be continuos and it could be out of range,...
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1answer
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static and dynamic data in clinical trials

Hi everybody and thanks in advance for those who will help me for this problem. I have multiple data regarding patients involved in a clinical trial and my goal is to predict their death/non death. ...
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1answer
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Prediction for not completely well classified data

I have a DataFrame of users, some of them are "bots" and they are identified with a bit equal to 1 in the "is_bot" column, if the bit is 0, the user is considered as "human". The problem is that some ...
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Iterative Reweighted Least Squares in python

I am trying to manually implement the irls logistic regression (Chapter 4.3.3 in Bishop - Pattern Recognition And Machine Learning) in python. For updating the weights, I am using $w' = w-(\Phi^TR\...
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How do you distinguish between conversational text and possible news article?

Context When you receive messages in group chats, how do you detect if that message belongs to conversational dialogue or if it is a 'news' article (could be fake or real) that they are sharing? ...
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How to calculate final AUC for sequential combinations of binary classification models in Python

I am working on developing a binary classification model using GradientBoostingClassifier on a highly imbalanced dataset (100:1) that I plan to implement in 2 steps. build a model (M1) that will try ...
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SKLearn NearestCentroidClassifier score with predict_proba

I'm using the NearestCentroidClassifier combined with TF-IDF for classification of documents. The are linked to a growing number of document groups. I've set sklearns TfIdfVectorizer and the ...
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Data points are highly overlapped and do not follow smoothness rule assumption

I am working on a very high dimensional categorical features based data set. There are two output classes and 2-dimensional PCA plot suggests that the data points belonging to both +ve and -ve classes ...
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loss increases but accuracy and macroF1 are still stable and don't change dramatically

I have a classification task with 2 classes. The dataset is imbalanced. When I train the model, at some point, the loss of test dataset starts to increase but the values of accuracy and macro-f1 don't ...
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1answer
26 views

Deciding what type of model to use for predicting the bottom decile of student grades

I have a large dataset which includes 36 variables (in %iles) to describe a student, and then the output is the students grades as a %ile. I am trying to predict, using the 36 variables, whether a ...
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What is the best possible method/methods to determine best possible branch(rule) in a decision tree plot for the positive cases only?

I have a dataset, which I am using for loan prediction. Thus, it is pretty much clear that my dataset is imbalanced. I have used Decision Tree to plot the tree structure. Now, I want to find the ...
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1answer
17 views

When exactly should I use weighted loss?

Do I have to use it in any case when the class distribution is imbalance (Train: class A:10%, B:90% and Test: class A:10%, B:90%) or when it is different (Train: class A:10%, B:90% and Test: class A:...
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1answer
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How to use ontologies for text classification?

I am new to machine learning and want to classify sentences using ontologies (taxonomies/ knowledge graphs) and supervised learning methods (I have an annotated training dataset). My question is how ...
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Does a neural network implicitly consider interaction between features?

For example, I am looking at a dataset with student grades as labels and the features include socioeconomic variables, height, weight, county, extracurricular involvement etc. One observation that I ...
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How to deal with images with textual noise?

I have a dataset of images collected from google and bing images (scraped). basically I want to classify these images into binary classes (positive, negative). Images that contain a text originally ...
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Can a classifier be trained with reinforcement learning without access to single classification results?

Question: Can a classifier be trained with reinforcement learning without access to single classification results? I want to train a classifier using reinforcement learning. However, there is one big ...

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