Statistical classification is the problem of identifying the sub-population to which new observations belong, where the identity of the sub-population is unknown, on the basis of a training set of data containing observations whose sub-population is known. Therefore these classifications will show a ...

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15 views

Data classification and regression in R

My question is about finding the relationship between speed of a vehicle and its emission. I think, based on the nature of this problem, there is not a constant relationship between the speed of a ...
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1answer
23 views

How to compare the results of two hierarchical classification results?

How could I compare two classification results from two implementations of e.g. using different methods/features for pairwise similarity calculation? If one method classified e.g. 3000 data points ...
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2answers
33 views

What is the purpose of including negative samples in a training set?

I am new to machine learning and advanced statistics (anything beyond first-year college statistics), and I have been exploring the effectiveness of various classifiers on modeling different hand ...
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0answers
30 views

What is the FASTEST Neural Network Command in R? [on hold]

I'm running Neural Network on a data frame with 40,000 observations, 7500 predictors and with one response variables. The response variable is a categorical variable with 4 levels. I've found a lot ...
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18 views

How to select important features with multiple datasets?

There are several feature selection methods for dataset, but it is difficult for me to find methods for multiple datasets. The example of my datasets is like below. Sensors are features, and they are ...
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0answers
10 views

Unbalanced classification problem: Can more data be more harmful?

In a two-class problem, I have 1000 raw data to train my model, which 900 belongs to class 1 and 100 belongs to class 2. If I add another 500 data into the pool but they all belong to class 1, will ...
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1answer
31 views

Classification of binary string into 0 or 1 categories

I observe a binary string that contains both 0's and 1's like this one 100111101. However the true process that created these strings are either all 1's or all 0's. Due to technical errors and ...
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13 views

Statistical Approaches for Uncertain Target Variable

Let me first explain what I have tried to mean by uncertain target variable. Consider the following scenario: Illegal immigrant behavior: Suppose you want to interdict with illegal immigrants to ...
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9 views

Obtaining exact output values of SMO based classification, before clearly demarcating them into classes

While running the SMO classifier in weka, if I have inputted my training labels as 0 and 5, (A binary set), then while running the classifier model on test data, are the outputs some decimal values ...
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10 views

Difference between One Rule and Learn One Rule

What are the differences between One Rule and Learn One Rule? Learn One Rule goes deeper in the decision tree (and, like 1R, tests on all the attributes), is this correct? Thanks!
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1answer
20 views

Classification eda

I am constructing a 2-class classifier and using cross validation to tune certain parameters in my model. The predictor variables are both continuous and one is ordinal. Based on looking at the ...
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0answers
20 views

Factors for binary variables [on hold]

Is it necessary to convert binary variables to factors? Or should I do it only if I have categorical (>2 levels) vars? Also, does it depend on whether a binary variable is a predictor or a response ...
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1answer
30 views

Calculating Costs for ROC Curves

I am trying to calculate the optimal threshold for a binary classifier using Receiver operating characteristic (ROC) Curves. Currently I am assigning a cost for each false negative and another cost ...
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42 views

Big difference of accuracy between C-SVC and nu-SVC using SVM

I'm currently dealing with an image classification problems. The objective is to classify the images to 4 classes, with 8000 images in the training set and 14000 images to predict. I'm using the SVM ...
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0answers
15 views

How to separate two classes when the features values predicting them are so similar ?

What should be my approach. I got 13 principal components from 21 numerical features. The 13 features have a gaussian distribution. The plot below is between the top two components. Should I clean the ...
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1answer
32 views

Which are the suitable classification algorithms when the number of categories are more than 1000

Combination of categories is not possible as each class is a distinct brand. One similar challenge was classifying objects using images (link below) but I could get any specific direction from few ...
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2answers
65 views

Can a classifier trained with oversampled data be used to classify unbalanced data

I am developing a random forest model for predicting fraudulent credit card transactions. I have made a train and test split in my dataset, and finally chosen a model through different metrics, ...
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1answer
58 views

Feature/variable selection with categorical variables [closed]

My goal is to compare several machine learning algorithms for sales prediction (logistic regression, neural network, random forest, svm -> classification problem, whether the sales will go up or down) ...
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0answers
14 views

Using SKlearn SelectKBest f_classif for categorical values [closed]

I am trying to use SelectKBest f_classif on my data, which is of the following format: each observation is composed of a d-dimensional vector(d different features) and is associated with a label ...
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0answers
18 views

removing outliers in poisson distributed data (count data) to improve classifier accuracy [on hold]

I wanted to know how to remove outliers in count data, the data i have is labeled can I remove the outliers from each class ? Or consider only 80% of the data close to the cluster center to train the ...
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0answers
14 views

Learning from Multiple Naive Annotator's Presence (and their labels as Y)

Although I am new to the forum, I have been following for several years and wanted to begin by sincerely thanking everyone, from the administrators to the members, for creating such an amazing ...
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0answers
15 views

Suspicious value of ks value in text classification problem

I am working on a text classification problem where the data set is highly imbalanced, with only 5 percent positive samples. Total size of the data set is also small at 1300 records. I tried ...
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0answers
13 views

What does mean by the number of pixel positions in CNN

I am doing project in machine learning using deep CNN. I need to understand how to choose hyperparameters (number of filter, shape of filter, max pooling shaep,..). I am using image of 42*42 (...
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0answers
11 views

how to use a Bayes Network Classifier weka model in our application

we are working on a Persian news summarizer algorithm. we used Weka API and BayesNet classifier to build a model for predicting sentences to be include in summarized text. the output result of Bayes ...
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1answer
21 views

How to operate on a count dataset (positive whole numbers with a lot of zeros) using neural networks for classification?

So i have some dataset, which is basically a count dataset. I have my own code for the classification using neural networks. Turns out that the data does not have a lot of correlation so accuracies as ...
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0answers
4 views

Cross-validation on XGBClassifier for multiclass classification in python [migrated]

I'm trying to perform cross-validation on a XGBClassifier for a multi-class classification problem using the following code adapted from http://www.analyticsvidhya.com/blog/2016/03/complete-guide-...
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1answer
43 views

Algorithm: multi label classification

I am a biologist and I have an algorithm question, I asked on stack exchange but was suggested to come here. Also, I have really tried to explain my problem using simple toy data; note that in real ...
2
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1answer
79 views

TOO low estimated SVM probability for most of the negative test examples?

I am using LIBSVM (as well as the fitcsvm and fitSVMPosterior of ...
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0answers
29 views

Is it ok to use symmetric loss function when evaluation metric is asymmetric?

I completely understand that it's ok to use a loss function different from the evaluation metric. For example, accuracy isn't computationally feasible to optimize directly since it's not ...
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1answer
19 views

Statistical Test for Embedding?

I have a bunch of labeled objects. Let's say there are K classes altogether. Now suppose every object is mapped to a data point in R^n, or in other words, is embedded in R^n. Ideally a good embedding ...
2
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1answer
44 views

Can I use output of classifier A as feature for classifier B?

This is likely to be a confused question, but I'm curious if this is a valid way to combine classifiers. I have a classification data set, i.e. column of labels and N columns of features, and I use a ...
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0answers
34 views

Decontaminating the training data set - any idea why it works?

TL;DR I have to decontaminate a training data set that includes irrelevant observations that will harm the quality of any statistical estimation and inference. It is of course initially unknown which ...
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0answers
31 views

Classification technique for unsupervised data?

I have unsupervised data it is a mix of continuous and categorical data. Now I want to classify the test data into three categories on basis of my unsupervised data. The approach I took is first do ...
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0answers
7 views

Robuste classification of ground and no-ground points in dense forest LiDAR Airborne

This is my first project on my graduation so take easy. Im studying methods to remove outliers on LiDAR airbone data in the tropical amazon rainforest. Remove outliers above the trees is easy, the ...
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1answer
18 views

Classification when variables are observed as a group

How do I classify variables when the classifying binary output is known only for groups of variables? Here is a concrete example: a person eats different types of foods on different days, and she ...
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1answer
72 views

Partitioning training data for dimension reduction and classification

Let's say I want to test the performance of my dimension reduction + classification pipeline. To do this, I will use k-fold cross validation. I know that performing dimension reduction on the complete ...
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0answers
21 views

Evaluation of a ternary classifier

Are there standard evaluation procedures for non-binary classifiers? In my case I have "nested" classes, being absence and presence of an effect the first and usual binary categorization, but ...
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0answers
7 views

Many class, one train sample per class classification

I have a classification task involving one train sample per class with around 300 classes.Furthermore each observasion has about 200 features. Can anyone suggest an approach that might work better ...
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0answers
6 views

Classifier with Feedback in R

In many research talks, I have seen people talking about classifiers that accept human feedback. For example, the classifier will show you an image marked as "forest" and you can give your feedback ...
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1answer
11 views

Choosing cases for assisted supervised learning

I have a bag of words binary text classification task. The SGD algorithm performed well for a certain target where number of labeled cases for training reached tens of thousands. For another target ...
3
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2answers
68 views

How does Naive Bayes work with continuous variables?

To my (very basic) understanding, Naive Bayes estimates probabilities based on the class frequencies of each feature in the training data. But how does it calculate the frequency of continuous ...
2
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0answers
36 views

Help interpreting formula for multi-class hinge loss

As I'm reading from wikipedia, and this Cross Validated question: Gradient for hinge loss multiclass, the gradient value for a training feature set is somewhat straightforward. However if I'm ...
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1answer
24 views

Classification Problem with Big Unbalanced Data

I have a dataset with over 1 million observations, and a few dozens of predictors. My target variable is binary (gold customer vs. not gold customer) and I wish to build a classifier for prediction. ...
3
votes
1answer
58 views

What happens if I flip targets and predictions in cross-entropy?

When we compute the cross-entropy within the machine learning context, we use the following formula: $$ CE(t, p) = -\sum_{i=1}^{N} t_i \ \log(p_i) $$ Where $t$ is the target class probability, and $...
1
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1answer
25 views

What happens when I can't classify data?

I have following dataset: a1 a2 dec 2 1 0 0 0 1 4 0 1 8 0 0 4 0 1 4 1 0 6 0 0 2 0 0 4 0 1 4 1 1 Based on that, I've ...
3
votes
1answer
118 views

ideas on machine-learning algorithms to classify products

I have a list of products, including variables such as the product name (as it appears on the receipt) and the merchant where the product was bought. I have a good deal of them manually classified ...
2
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0answers
49 views

showing causality between police killings and demographics (i.e. race, class, gender, location)

I've got a whole lot of police data and was wondering what sort of approaches I could use to show strong correlation, and if possible, causal effects, between police killings/arrest & call-ins for ...
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0answers
11 views

How to use the dataset for leave-one-ut CV?

I would appreciate some feedback on my leave one out CV procedure, because I am not sure it works correctly. 1.Load the files I am using 26 binary classified articles. Files are shuffled when ...
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0answers
4 views

Relationship between recall and Precision-Recall curve

I'm trying to evaluate some classification algorithms' results in my imbalanced dataset. With imabalnced, I mean that there are much more negative labels than positive ones. Accuracy and precision are ...
1
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1answer
104 views

How to choose alpha values in CHAID?

In CHAID control parameters we have to specify the alpha value for Merging Threshold and Splitting Threshold. Typically this alpha (p-value) is set at 0.05. How do we select the alpha2 and alpha4 ...