Questions tagged [machine-learning]

Machine learning algorithms build a model of the training data. The term "machine learning" is vaguely defined; it includes what is also called statistical learning, reinforcement learning, unsupervised learning, etc. ALWAYS ADD A MORE SPECIFIC TAG.

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15
votes
1answer
1k views

Why can't a single ReLU learn a ReLU?

As a follow-up to My neural network can't even learn Euclidean distance I simplified even more and tried to train a single ReLU (with random weight) to a single ReLU. This is the simplest network ...
19
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2answers
16k views

Meaning of a neural network as a black-box?

I often hear people talking about neural networks as something as a black-box that you don't understand what it does or what they mean. I actually I can't understand what they mean by that! If you ...
13
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3answers
7k views

Perform linear regression, but force solution to go through some particular data points

I know how to perform a linear regression on a set of points. That is, I know how to fit a polynomial of my choice, to a given data set, (in the LSE sense). However, what I do not know, is how to ...
18
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2answers
17k views

Best way to perform multiclass SVM

I know that the SVM is a binary classifier. I would like to extend it to multi-class SVM. Which is the best, and maybe the easiest, way to perform it? code: in MATLAB ...
27
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5answers
13k views

Clustering procedure where each cluster has an equal number of points?

I have some points $X=\{x_1,...,x_n\}$ in $R^p$, and I want to cluster the points so that: Each cluster contains an equal number of elements of $X$. (Assume that the number of clusters divides $n$.) ...
10
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2answers
12k views

How to make Random Forests more interpretable? [duplicate]

Are there any methods that one could utilize to make Random Forest more interpretable? Random Forest performs much better than CART but it is a lot less interpretable.
11
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2answers
561 views

What is meant by the variance of *functions* in *Introduction to Statistical Learning*?

On pg. 34 of Introduction to Statistical Learning: $\newcommand{\Var}{{\rm Var}}$ Though the mathematical proof is beyond the scope of this book, it is possible to show that the expected test MSE, ...
8
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3answers
9k views

What balancing method can I apply to a imbalanced data set?

I'm trying to solve one classification problem from the UCI database repository. Unfortunately (or fortunately), I've noticed that my dataset is imbalanced. I've structured the data as 5 classes, ...
7
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3answers
2k views

Is a lower training accuracy possible in overfitting (one class SVM)

I am using the heart_scale data from LibSVM. The original data includes 13 features, but I only used 2 of them in order to plot the distributions in a figure. Instead of training the binary classifier,...
6
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1answer
704 views

What do “real values” refer to in supervised classification?

I'm using supervised classification algorithms from mlpy to classify things into two groups for a question-answering system. I don't really know how these algorithms work, but they seem to be doing ...
3
votes
1answer
863 views

How to do a bias-variance analysis on a machine learning modelling process

I searched on topics of the bias and variance trade-off and got back lots of questions with different levels of response. The information is scattering too much and unsystematic to answer my own ...
2
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1answer
546 views

Graphical path Coordinate Descent in case of semi-differentiable functions such as Lasso

I am trying to understand how the graphical solution path to the optimum would look in the case of Lasso Regression. I can find only Pictures for the differentiable or non differentiable case. The ...
2
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1answer
4k views

Time steps in Keras LSTM

My understanding of time-series LSTM training is that the recurrent cell gets unrolled to a specified length (num_steps), and parameter updates are back-propagated ...
71
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1answer
68k views

How to split the dataset for cross validation, learning curve, and final evaluation?

What is an appropriate strategy for splitting the dataset? I ask for feedback on the following approach (not on the individual parameters like test_size or ...
118
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2answers
123k views

Gradient Boosting Tree vs Random Forest

Gradient tree boosting as proposed by Friedman uses decision trees as base learners. I'm wondering if we should make the base decision tree as complex as possible (fully grown) or simpler? Is there ...
55
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5answers
48k views

Is it necessary to scale the target value in addition to scaling features for regression analysis?

I'm building regression models. As a preprocessing step, I scale my feature values to have mean 0 and standard deviation 1. Is it necessary to normalize the target values also?
54
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3answers
31k views

Perform feature normalization before or within model validation?

A common good practice in Machine Learning is to do feature normalization or data standardization of the predictor variables, that's it, center the data substracting the mean and normalize it dividing ...
51
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4answers
44k views

Why not approach classification through regression?

Some material I've seen on machine learning said that it's a bad idea to approach a classification problem through regression. But I think it's always possible to do a continuous regression to fit the ...
38
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3answers
30k views

Guideline to select the hyperparameters in Deep Learning

I'm looking for a paper that could help in giving a guideline on how to choose the hyperparameters of a deep architecture, like stacked auto-encoders or deep believe networks. There are a lot of ...
43
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6answers
26k views

Why downsample?

Suppose I want to learn a classifier that predicts if an email is spam. And suppose only 1% of emails are spam. The easiest thing to do would be to learn the trivial classifier that says none of the ...
29
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6answers
14k views

Variable selection procedure for binary classification

What are the variable/feature selection that you prefer for binary classification when there are many more variables/feature than observations in the learning set? The aim here is to discuss what is ...
28
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3answers
37k views

How to judge if a supervised machine learning model is overfitting or not?

Can anyone tell me how to judge if a supervised machine learning model is overfitting or not? If I don't have an external validation dataset, I want to know if I can use ROC of 10 fold cross ...
34
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4answers
35k views

What is the weak side of decision trees?

Decision trees seems to be a very understandable machine learning method. Once created it can be easily inspected by a human which is a great advantage in some applications. What are the practical ...
37
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3answers
21k views

Creating a “certainty score” from the votes in random forests?

I am looking to train a classifier that will discriminate between Type A and Type B objects with a reasonably large training set ...
31
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4answers
24k views

When should I balance classes in a training data set?

I had an online course, where I learned, that unbalanced classes in the training data might lead to problems, because classification algorithms go for the majority rule, as it gives good results if ...
36
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3answers
42k views

Things to consider about masters programs in statistics

It is admission season for graduate schools. I (and many students like me) am now trying to decide which statistics program to pick. What are some things those of you who work with statistics suggest ...
33
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6answers
5k views

Data mining: How should I go about finding the functional form?

I'm curious about repeatable procedures that can be used to discover the functional form of the function y = f(A, B, C) + error_term where my only input is a set of ...
26
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3answers
10k views

Topic models and word co-occurrence methods

Popular topic models like LDA usually cluster words that tend to co-occur together into the same topic (cluster). What is the main difference between such topic models, and other simple co-...
22
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1answer
3k views

Did I just invent a Bayesian method for analysis of ROC curves?

Preamble This is a long post. If you're re-reading this, please note that I've revised the question portion, though the background material remains the same. Additionally, I believe that I've devised ...
19
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2answers
3k views

Backpropagation algorithm

I got a slight confusion on the backpropagation algorithm used in multilayer perceptron (MLP). The error is adjusted by the cost function. In backpropagation, we are trying to adjust the weight of ...
16
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3answers
13k views

Area under the ROC curve or area under the PR curve for imbalanced data?

I have some doubts about which performance measure to use, area under the ROC curve (TPR as a function of FPR) or area under the precision-recall curve (precision as a function of recall). My data is ...
29
votes
1answer
45k views

One-vs-All and One-vs-One in svm?

What is the difference between a one-vs-all and a one-vs-one SVM classifier? Does the one-vs-all mean one classifier to classify all types / categories of the new image and one-vs-one mean each type /...
19
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6answers
11k views

Programmer looking to break into machine learning field

I am a software developer (mostly .NET and Python about 5 years experience). What can I do to help me get a job in the machine learning field or really anything that will get me started in that field? ...
28
votes
3answers
26k views

Unsupervised, supervised and semi-supervised learning

In the context of machine learning, what is the difference between unsupervised learning supervised learning and semi-supervised learning? And what are some of the main algorithmic approaches to ...
15
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3answers
9k views

Suggestions for cost-sensitive learning in a highly imbalanced setting

I have a dataset with a few million rows and ~100 columns. I would like to detect about 1% of the examples in the dataset, which belong to a common class. I have a minimum precision constraint, but ...
25
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5answers
27k views

Machine learning algorithms to handle missing data

I am trying to develop a predictive model using high-dimensional clinical data including laboratory values. The data space is sparse with 5k samples and 200 variables. The idea is to rank the ...
19
votes
5answers
23k views

How to recode categorical variable into numerical variable when using SVM or Neural Network

To use SVM or Neural Network it needs to transform (encode) categorical variables into numeric variables, the normal method in this case is to use 0-1 binary values with the k-th categorical value ...
9
votes
1answer
2k views

Least angle regression keeps the correlations monotonically decreasing and tied?

I'm trying to solve a problem for least angle regression (LAR). This is a problem 3.23 on page 97 of Hastie et al., Elements of Statistical Learning, 2nd. ed. (5th printing). Consider a regression ...
19
votes
6answers
9k views

Is hyperparameter tuning on sample of dataset a bad idea?

I have a dataset of 140000 examples and 30 features for which I am training several classifiers for a binary classification (SVM, Logistic Regression, Random Forest etc) In many cases hyperparameter ...
11
votes
1answer
9k views

Scikit Binomial Deviance Loss Function

This is scikit GradientBoosting's binomial deviance loss function, ...
12
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3answers
2k views

Is PCA optimization convex?

The objective function of Principal Component Analysis (PCA) is minimizing the reconstruction error in L2 norm (see section 2.12 here. Another view is trying to maximize the variance on projection. We ...
9
votes
1answer
2k views

When is a proper scoring rule a better estimate of generalization in a classification setting?

A typical approach to solving a classification problem is to identify a class of candidate models, and then perform model selection using some procedure like cross validation. Typically one selects ...
9
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3answers
22k views

What exactly is a hypothesis space in machine learning?

Whilst I understand the term conceptually, I'm struggling to understand it operationally. Could anyone help me out by providing an example?
17
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4answers
7k views

What does “degree of freedom” mean in neural networks?

In Bishop's book "Pattern Classification and Machine Learning", it describes a technique for regularization in the context of neural networks. However, I don't understand a paragraph describing that ...
11
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2answers
5k views

machine learning techniques for longitudinal data

I was wondering if there were any machine learning techniques (unsupervised) for modelling longitudinal data? I've always used mixed effects models (mostly non-linear) but I was wondering if there are ...
30
votes
4answers
38k views

How do you Interpret RMSLE (Root Mean Squared Logarithmic Error)?

I've been doing a machine learning competition where they use RMSLE (Root Mean Squared Logarithmic Error) to evaluate the performance predicting the sale price of a category of equipment. The problem ...
30
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3answers
3k views

Utility of feature-engineering : Why create new features based on existing features?

I often see people create new features based on existing features on a machine learning problem. For example, here : https://triangleinequality.wordpress.com/2013/09/08/basic-feature-engineering-with-...
13
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1answer
6k views

Supervised dimensionality reduction

I have a data set consisting of 15K labeled samples (of 10 groups). I want to apply dimensionality reduction into 2 dimensions, that would take into consideration the knowledge of the labels. When I ...
7
votes
1answer
2k views

Cross-validation for timeseries data with regression

I am familiar with "regular" cross-validation, but now I want to make timeseries predictions while using cross-validation with a simple linear regression function. I write down a simple example, to ...
6
votes
2answers
909 views

Is dimensionality reduction almost always useful for classification?

Is singular value decomposition almost always useful in practice for enhancing the predicative power of a trained classification model? E.x. A dataset for classification has 20,000 features. Run SVD ...