Questions tagged [classification]

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 variable behavior which can be studied by statistics.

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3answers
6k views

Classifier with adjustable precision vs recall

I am working on a binary classification problem where it is much more important to not have false positives; quite a lot of false negatives is ok. I have used a bunch of classifiers in sklearn for ...
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4answers
29k views

What is a Classifier?

I cannot find the general definition of what is a classifier? I understand how it can work, but I can't come to a definition.
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1answer
5k views

Pros of Jeffries Matusita distance

According to some paper I am reading, Jeffries and Matusita distance is commonly used. But I couldn't find much information on it except for the formula below JMD(x,y)=$\sqrt[2]{\sum(\sqrt[2]{x_i}-\...
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3answers
5k views

Confidence interval for cross-validated classification accuracy

I'm working on a classification problem that computes a similarity metric between two input x-ray images. If the images are of the same person (label of 'right'), a higher metric will be calculated; ...
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2answers
9k views

Organizing a classification tree (in rpart) into a set of rules?

Is there a way that once a complex classification tree is constructed using rpart (in R), to organize the decision rules produced for each class? So instead of getting one huge tree, we get a set of ...
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3answers
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Importance of variables in logistic regression

I am probably dealing with a problem that has probably been solved a hundred times before, but I'm not sure where to find the answer. When using logistic regression, given many features $x_1,...,x_n$ ...
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1answer
3k views

Evaluation of classifiers: learning curves vs ROC curves

I would like to compare 2 different classifiers for a multiclass text classification problem that use large training datasets. I am doubting whether I should use ROC curves or learning curves to ...
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3answers
2k views

Classification model for movie rating prediction

I am somewhat new to data mining, and I am working on a classification model for movie rating prediction. I have collected data sets from IMDB, and I am planning to use a decision trees and nearest ...
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3answers
5k views

What loss function should one use to get a high precision or high recall binary classifier?

I'm trying to make a detector of objects that occur very rarely (in images), planning to use a CNN binary classifier applied in a sliding/resized window. I've constructed balanced 1:1 positive-...
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2answers
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Using Adaboost with SVM for classification

I know that Adaboost tries to generate a strong classifier using a linear combination of a set of weak classifiers. However, I've read some papers suggesting Adaboost and SVMs work in harmony (even ...
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2answers
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How to understand a convolutional deep belief network for audio classification?

In "Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations" by Lee et. al.(PDF) Convolutional DBN's are proposed. Also the method is evaluated for image ...
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2answers
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Why is Bayes Classifier the ideal classifier?

It is considered the ideal case in which the probability structure underlying the categories is known perfectly. Why is that with Bayes classifier we achieve the best performance that can be achieved ...
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1answer
8k views

When to use Gini impurity and when to use information gain?

Can someone please explain to me when to use Gini impurity and information gain for decision trees? Can you give me situations/examples of when is best to use which?
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1answer
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How do we predict rare events?

I am working on developing an insurance risk predictive model. These models are of "rare events" like airline no-show prediction, hardware fault detection, etc. As I prepared my data set, I tried to ...
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1answer
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Motivation behind random forest algorithm steps

The method that I'm familiar with for constructing a random forest is as follows: (from http://www.stat.berkeley.edu/~breiman/RandomForests/cc_home.htm) To build a tree in the forest we: Bootstrap a ...
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1answer
4k views

Reducing number of levels of unordered categorical predictor variable

I want to train a classifier, say SVM, or random forest, or any other classifier. One of the features in the dataset is a categorical variable with 1000 levels. What is the best way to reduce the ...
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1answer
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Stratified classification with random forests (or another classifier)

So, I've got a matrix of about 60 x 1000. I'm looking at it as 60 objects with 1000 features; the 60 objects are grouped into 3 classes (a,b,c). 20 objects in each class, and we know the true ...
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1answer
15k views

How to choose the cutoff probability for a rare event Logistic Regression

I have 100,000 observations (9 dummy indicator variables) with 1000 positives. Logistic Regression should work fine in this case but the cutoff probability puzzles me. In common literature, we ...
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1answer
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calculation threshold for minimum risk classifier?

Suppose Two Class $C_1$ and $C_2$ has an attribute $x$ and has distribution $ \cal{N} (0, 0.5)$ and $ \cal{N} (1, 0.5)$. if we have equal prior $P(C_1)=P(C_2)=0.5$ for following cost matrix: $L= \...
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3answers
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Are there any libraries available for CART-like methods using sparse predictors & responses?

I'm working with some large data sets using the gbm package in R. Both my predictor matrix and my response vector are pretty sparse (i.e. most entries are zero). I was hoping to build decision trees ...
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2answers
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Classification with partially “unknown” data

Suppose I want to learn a classifier that takes a vector of numbers as input, and gives a class label as output. My training data consists of a large number of input-output pairs. However, when I ...
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2answers
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Incremental learning for classification models in R

Assume, I have a classifier (It could be any of the standard classifiers like decision tree, random forest, logistic regression .. etc.) for fraud detection using the below code ...
11
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1answer
5k views

Averaging precision and recall when using cross validation

I have performed classification using multiple classifiers for a 2-classes labelled data, and I used 5-fold cross validation. For each fold I calculated tp, tn, fp, and fn. Then I calculated the ...
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2answers
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Measures of class separability in classification problems

An example of a good measure of class separability in linear discriminant learners is Fisher's linear discriminant ratio. Are there other useful metrics to determine if feature sets provide good class ...
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1answer
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Extending 2-class models to multi-class problems

This paper on Adaboost gives some suggestions and code (page 17) for extending 2-class models to K-class problems. I would like to generalize this code, such that I can easily plug in different 2-...
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5answers
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Why should binning be avoided at all costs?

So I've read a few posts about why binning should always be avoided. A popular reference for that claim being this link. The main getaway being that the binning points (or cutpoints) are rather ...
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3answers
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When would you use PCA rather than LDA in classification?

I'm reading this article on the difference between Principle Component Analysis and Multiple Discriminant Analysis (Linear Discriminant Analysis), and I'm trying to understand why you would ever use ...
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2answers
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Why Adaboost with Decision Trees?

I've been reading a bit on boosting algorithms for classification tasks and Adaboost in particular. I understand that the purpose of Adaboost is to take several "weak learners" and, through a set of ...
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2answers
17k views

K-nearest-neighbour with continuous and binary variables

I have a data set with columns a b c (3 attributes). a is numerical and continuous while b...
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2answers
21k views

Applying PCA to test data for classification purposes

I've recently learned about the wonderful PCA and I've done the example outlined in scikit-learn documentation. I am interested to know how I can apply PCA to new data points for classification ...
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1answer
7k views

ROC curves for unbalanced datasets

Consider an input matrix $X$ and a binary output $y$. A common way to measure the performance of a classifier is to use ROC curves. In a ROC plot the diagonal is the result that would be obtained ...
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3answers
992 views

Where did the term “learn a model” come from

Often I have heard the data miners here use this term. As a statistician who has worked on classification problems I am familiar with the term "train a classifier" and I assume "learn a model" means ...
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4answers
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Overfitting with Linear Classifiers

Today our professor stated in class that "overfitting with linear classifiers is not possible". I hold that to be wrong, since even linear classifiers can be sensitive to outliers in the training set -...
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3answers
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How to classify a unbalanced dataset by Convolutional Neural Networks (CNN)?

I have a unbalanced dataset in a binary classification task, where the positives amount vs negatives amount is 0.3% vs 99.7%. The gap between positives and negatives are huge. When I train a CNN with ...
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4answers
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Improving the SVM classification of diabetes

I am using SVM to predict diabetes. I am using the BRFSS data set for this purpose. The data set has the dimensions of $432607 \times 136$ and is skewed. The percentage of ...
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2answers
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How to change threshold for classification in R randomForests?

All the Species Distribution Modelling literature suggests that when predicting the presence/absence of a species using a model that outputs probabilities (e.g., RandomForests), choice of the ...
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2answers
11k views

Is f-measure synonymous with accuracy?

I understand that f-measure (based on precision and recall) is an estimate of how accurate a classifier is. Also, f-measure is favored over accuracy when we have an unbalanced dataset. I have a simple ...
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2answers
36k views

How to build a confusion matrix for a multiclass classifier?

I have a problem with 6 classes. So I build a multiclass classifier, as follows: for each class, I have one Logistic Regression classifier, using One vs. All, which means that I have 6 different ...
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3answers
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How to visualize Bayesian goodness of fit for logistic regression

For a Bayesian logistic regression problem, I have created a posterior predictive distribution. I sample from the predictive distribution and receive thousands of samples of (0,1) for each observation ...
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2answers
9k views

Machine Learning technique for learning string patterns

I have a list of words, belonging to different selfdefined categories. Each category has its own pattern (for example one has a fixed length with special characters, another exists of characters which ...
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4answers
4k views

Classifier for uncertain class labels

Let's say I have a set of instances with class labels associated. It does not matter how these instances were labelled, but how certain their class membership is. Each instancs belongs to exactly one ...
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1answer
16k views

How to use decision stump as weak learner in Adaboost?

I want to implement Adaboost using Decision Stump. Is it correct to make as many decision stump as our data set's features in each iteration of Adaboost? For example, if I have a data set with 24 ...
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3answers
22k views

What is a good AUC for a precision-recall curve?

Because I have a very imbalanced dataset (9% positive outcomes), I decided a precision-recall curve was more appropriate than an ROC curve. I obtained the analogous summary measure of area under the P-...
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1answer
687 views

Are MFCCs the optimal method of representing music to a retrieval system?

A signal processing technique, the Mel frequency Cepstrum, is often used to extract information from a musical piece for use in a machine learning task. This method gives a short-term power spectrum, ...
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1answer
2k views

Do neural networks usually take a while to “kick in” during training?

I am trying to train a deep neural network for classification, using back propagation. Specifically, I am using a convolutional neural network for image classification, using the Tensor Flow library. ...
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3answers
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RBF SVM use cases (vs logistic regression and random forest)

Support Vector Machines with radial-base function kernel is a general-purpose supervised classifier. While I know theoretical foundations for these SVMs, and their strong points, I am not aware of ...
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1answer
548 views

Test for proportions and binary classifier

I have a prototype machine producing parts. In a first test the machine produces $N_1$ parts and a binary classifier tells me that $d_1$ parts are defective ($d_1 < N_1$, usually $d_1/N_1<0.01$ ...
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3answers
12k views

Outlier detection in very small sets

I need to get as accurate as possible a value for the brightness of a mainly stable light source given twelve sample luminosity values. The sensor is imperfect, and the light can occasionally "flicker"...
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1answer
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On cophenetic correlation for dendrogram clustering

Consider the context of a dendrogram clustering. Let us call original dissimilarities the distances between the individuals. After constructing the dendrogram we define the cophenetic dissimilarity ...
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3answers
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How to compare the accuracy of two different models using statistical significance

I am working on time series prediction. I have two data sets $D1=\{x_1, x_2,....x_n\}$ and $D2=\{x_n+1, x_n+2, x_n+3,...., x_n+k\}$. I have three prediction models: $M1, M2, M3$. All of those model ...