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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7 views

Python: In which cases will random forest and SVM classifiers can produce high accuracy?

I am using Random Forest and SVM classifiers to do classification, and I have 18322 samples which are unbalanced in 9 classes (3667, 1060, 1267, 2103, 2174, 1495, 884, 1462, 4210). I use 10-fold CV ...
2
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1answer
21 views

Using Poisson process model for prediction?

Suppose some event $X$ occurs on average $10$ times per minute. The events are independent of each other. Now, if I have understood correctly, this can be modeled as a Poisson process, and I can ask ...
0
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0answers
8 views

Datasets with a specific decision structure [on hold]

I'm looking for classification datasets where the prediction variable has a certain structure, i.e. if the possible prediction outcomes are natural numbers between 1 and 10, the user is more preferred ...
0
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0answers
13 views

How to check if a sample is representative or not biased?

We are studying the difference in behavior between genders on an online community. We are only interested on those users who participate in the site and whose gender could be easily inferred by other ...
2
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1answer
19 views

What is the chance level accuracy in unbalanced classification problems?

Suppose one has a balanced classification problem (50% of 0's and 50% of 1's). In such a case, the so called chance-level accuracy of classifier would be 50%. What is the chance-level accuracy if the ...
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1answer
17 views

How to handle unknown class during classification

Given a classification task: Training dataset "A" with labelled data of 10 classes. Training dataset "B" with unlabelled data of 11 classes. Compare to "A" , "B"contains one extra class, we can ...
1
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1answer
38 views

Feature engineering

I recently realized, that feature engineering (designing input vectors for machine-learning algorithm) is one of the most complicated tasks when applying known algorithms (for example kernel ...
0
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1answer
19 views

Naive Bayes Binary Classification with Binary Features

I have a dataset with two classes $C_0$ and $C_1$. I have around $10$ to $20$ features that take binary values (either $0$ or $1$). My dataset has around $10000$ instances, with only a hundred of ...
2
votes
2answers
59 views

Representative elements of a set

I'm looking for the technical name of the following problem. It sounds like a standard machine learning technique, but I'm not familiar with the field, and can't seem to find it. Let's say that we ...
0
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1answer
48 views

Which machine learning model is applicable to the following case

I want to build a model that recognizes the species based on multiple indicators. The problem is, neural networks (usually) receive vectors, and my indicators are not always easily expressed in ...
1
vote
1answer
30 views

Can adding an additional feature to a perceptron classifier make the results worse?

I am using perceptron to solve a classification problem. I have a limited amount of features (26) and iterate through all possible combinations of them. A combination of two features [feature_a, ...
5
votes
1answer
85 views

Do CART trees capture interactions among predictors?

This paper claims that in CART, because a binary split is performed on a single covariate at each step, all splits are orthogonal and therefore interactions among covariates are not considered. ...
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0answers
9 views

Variable representations for faster learning convergence

My notes on machine learning state that transforming a classification problem from 2 classes, class A = 0, and class B = 1, to class A = $(1,0)$, and class B = $(0,1)$ leads to faster convergence in ...
1
vote
1answer
17 views

Is there ever any reason to discretise continuous ground truth if doing classification?

Is there a case where discretising continuous response improves classification performance? For example: A response variable is in the range 0 to 99. There are 10 classes defined by the following ...
0
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2answers
47 views

Categorical as a dependent variable in regression

I am trying to use a regression model which can predict the category of an object.One object has many variables (these are used in the model as independent variables). My question is what kind of ...
-1
votes
1answer
24 views

What are the benefits for semi-supervised learning over unsupervised clustering? Or any limitations?

I have another question about semi-supervised learning vs unsupervised clustering, what are the benefits and limitations? I have got some data with labels and some without labels. I performed ...
1
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0answers
16 views

Semi-supervised learning vs supervised learning, what are the benefits and limitations?

Just wondering if any previous work compared semi-supervised learning vs supervised learning? Currently, I have got both datasets with and without labeling. And therefore, it is intuitive for me to ...
1
vote
0answers
11 views

Learning over Multinomial data

I have a training data with 68 features... Each of which is a different multinomial distribution. Eg. Feature 1 can take 1 of 4 values while feature 2 can take one of 10 values. Which classifier or ...
0
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0answers
7 views

Apache Mahout Classification [closed]

i need a corpus to try mahout classification, i've tried the AG's corpus of news articles downloaded from this site http://www.di.unipi.it/~gulli/AG_corpus_of_news_articles.html but that was not ...
1
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0answers
7 views

Is it possible to apply SVM on one dimensional dataset in R? [closed]

Is it possible to apply SVM on one dimensional dataset in R? if yes, please suggest me the procedure.
0
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1answer
22 views

Isn't leave-1-out insufficient for proper classification evaluation?

I encountered several papers that used some classification method (for instance, LDA), with leave-1-out validation, and posted the classification results as an aggregation of all results (for all ...
0
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0answers
17 views

Decision Tree in test, a Wrong Problems? [closed]

I took a test two days ago. one of our question is as follows: decision tree with depth 2 is constructed for two binary feature. hypothesis spase that can be shown with the following tree has ...
4
votes
1answer
63 views

Using kernels with Fisher's linear discriminant analysis

I am a bit stuck implementing the Kernel Fisher Discriminant. $$ J(\mathbf{w}) = \frac{\mathbf{w}^{\text{T}}\mathbf{S}_B^{\phi}\mathbf{w}}{\mathbf{w}^{\text{T}}\mathbf{S}_W^{\phi}\mathbf{w}} $$ $$ ...
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0answers
16 views

K-cross validation and Naive Bayes

I am doing an exercise of machine learning, and I have built a Gaussian Naive Bayes classifier (i.e., I have defined values of mean and standard deviation) using scikit-learn. Now I am supposed to ...
1
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1answer
32 views

Least-squares training error

In classification problems, the training error typically decreases as further training examples are acquired. However, in my current least-squares problem, the training error actually increases as ...
2
votes
1answer
43 views

Simple way for histograms classification

I'm trying to classify a histogram. I have 4 classes and I generate 4 histograms (h1, h2, h3 and h4) for each class. Each histogram contains 10 bins (attributes describing an object) on the x-axis and ...
1
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0answers
38 views

Support Vector Machines vs KNN

It was my understanding that in a separable case, SVMs produce the best separation possible and therefore will always produce the same or a better classification rate compared with say, 1NN, ...
0
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0answers
18 views

Full Information Maximum Likelihood, Imputation and Classification

I need to do a classification of a dataset, I have some missing data and I would like to try some "missing data techniques" to achieve the best accuracy. I already tried multiple imputation and ...
2
votes
0answers
50 views

Deep learning: representation learning or classification?

For classification, I have often heard about deep learning / deep neural networks as a form of representation learning. I am confused as to what "representation learning" means in this context. Which ...
3
votes
1answer
27 views

why do decision tree packages convert factor variable into two binary variables

Why are decision tree packages like say, rpart slow with increasing the number of factor levels in R. I read that it basically converts each factor variable into two binary variables representing ...
0
votes
0answers
8 views

SVM-Light displays corrupted precision/recall results

I run SVM-Light classifier but the recall/precision row it outputs seem to be corrupted: ...
1
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1answer
16 views

Linear Discriminant Analysis for $p=1$

I'm studying 'Introduction to Statistical Learning' by James, Witten, Hastie, Tibshirani. In page 139, of their book, they began by introducing Bayes' Theorem $p_k(X)=P(Y=k|X=x) = ...
0
votes
0answers
30 views

SVM output to probabilistic affiliation

How can I convert the svm output for multiple class classification(one vs one approach) to probabilistic values? Meaning that I want to have a probability for a tested element to be in each available ...
1
vote
1answer
23 views

How to interpret + and - precisions and recalls?

I understand the general calculation and concept of precision and recall. But when I am trying to predict people's ethnicity using some feature, say for example, predicting a binary class Chinese vs ...
3
votes
0answers
17 views

Ideas to classify shipping addresses

I have a dataset of addresses for a bunch of users. I need to classify an address into residential/commercial or office/educational. Moreover, every user has multiple addresses. So every user has a ...
3
votes
1answer
57 views

Effect of categorical interaction terms with random forest machine learning algorithm

Thanks in advance for the help. I have moderately large dataset (around 7000 samples) with numerous categorical predictors and a single binary response. All of the predictors are categorical. ...
0
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0answers
17 views

Estimating class probabilities given discriminative functions per class

What is the effective way to estimating class probabilities per class, if I know discriminative functions for each class (I have trained ML models giving some scores). My naive implementation is to ...
1
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0answers
24 views

How to draw plot of the values of decision function of multi class svm versus another arbitrary values?

I am trying to draw a plot of the decision function ($f(x)=sign(wx+b)$ which can be obtain by fit$decision.values in R using the svm function of e1071 package) versus another arbitrary values. From ...
0
votes
1answer
41 views

Interpret learning curves

I am training a Decision Tree on a dataset of around 580.000 data points. I took the following steps: Split the dataset in training (75%) and validation (25%) set. Determined the best depth for the ...
0
votes
0answers
13 views

How use F1 score in an unbalanced binary classification problem?

I have two trained models (MLP and SVM) that want check on unbalanced binary samples (out of sampl - True samples =3000, False samples = 200). I found that i can use ...
0
votes
0answers
27 views

Area under ROC curve vs. Accuracy in unbalanced sample

I have a binary classification problem with 3000 samples (number of 1 as outputs = 300, number of 0 as outputs = 1700). After balancing database (selecting 300 samples from 0 outputs) I trained the ...
0
votes
1answer
24 views

Classifier with variable number of features

I am trying to make a classifier when each sample has a variable number of features. An example of how this could occur is, for example, if the features are the purchases (type, dollar amount, etc) ...
0
votes
0answers
12 views

Can Platt Scaling to calibrate probabilities be used for classifiers other than SVM?

I am using Gaussian Mixture Models as classifiers and I compute posterior probabilities from them for a 2 class problem. However, the probabilities are pushed towards 0 and 1 due to very skewed ...
0
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0answers
6 views

Classifier with interchangeable features

I have a situation in which the features used in a classifier are multiple instances of the same kind of measurement, in random (or unknown) order; thus, a sample x1, x2, ... xn -> classA could with ...
7
votes
5answers
331 views

How to calculate Area Under the Curve (AUC), or the c-statistic, by hand

I am interested in calculating area under the curve (AUC), or the c-statistic, by hand for a binary logistic regression model. For example, in the validation dataset, I have the true value for the ...
1
vote
1answer
14 views

What kind of general strategy can you apply after selecting model and hyper parameter training?

As a rookie to machine learning area, I tried to play some Data Science tutorials and beginner competitions to gain some knowledge and experience. The problem I encountered in every scenarios is ...
0
votes
0answers
22 views

Classifier suggestion(27 dimensions, 9 classes)

The restriction of my classification problem is: 27 dimensions, 9 classes, 50.000 entries in the training set, 150.000 in test set. I need a machine learning classifier(open source code) that fits on ...
0
votes
0answers
14 views

How to drop variables in statistical classification analysis?

Given a set of data with variables and a training set, we can proceed classification analysis using Mahalanobis distance etc.(discriminant methods) But how do we know whether all these given ...
1
vote
0answers
21 views

Best Random Forest model converging to bagging: What does it mean? (R)

I am performing a grid search to tune the Random Forest parameters m and nodesize. I have 79 variables, and the best model, in terms of OOB error, is a model with 76 variables (OOB error = 0.137). So, ...
0
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0answers
10 views

basic implementation of Non-parametric Bayesian model in python

I am having a problem in understanding infinite Bayesian model with its implementation. I have tried looking scikit-learn package of python, DPGMM. I dont know why there is an argument for defining ...