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Questions tagged [machine-learning]

Methods and principles of building "computer systems that try to automatically improve with experience."

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Stacked shallow autoencoders vs. deep autoencoders

In LeCun et. all "Deep Learning", Chapter 14, page 506, I found the following statement: "A common strategy for training a deep autoencoder is to greedily pretrain the deep architecture by training a ...
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6 views

Extracting correlated data

Lets say I have a dataset like this image here https://imgur.com/oKXBX8q. The top figure is a histogram of the underlying data points which tend to be distributed vertically, horizontal, and at some ...
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10 views

Incorrect practice of data preprocessing

Suppose I have a dataset and split it into big_train and test set, as usual. Now if I split further the big_train set into small_train and validation set, suppose I use PCA, there are $2$ approaches: ...
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20 views

Mathematically, what are the drawbacks of R-squared in evaluation a regression model?

I kept seeing articles about the drawbacks of R-squared (and that's why we need to have adjusted R-squared). One drawback is that: "Every time you add a predictor to a model, the R-squared increases,...
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1answer
19 views

Problem with the word 'machine' in the definition of machine learning by Mitchell in the book “Machine Learning”

The definition : A computer program is said to learn from experience E with respect to some task T and performance measure P, if its performance at task T, as measured by P, improves with experience ...
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6 views

How to find the distance confidence between the sum of random variables and a given target value?

Given a distribution X($E[X]$, $Var[X]$) and a target value T, I am wondering how to find a value N such that $|\sum_{i=1}^{i=N}X_i - T|$ is minimized. (i) For a large T, it makes sense to pick $N = ...
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10 views

Machine learning for product names

I have a machine learning challenge I may be over thinking. I have a set of 3.5 million products (not unique, there are multiple instances of each product). Each product has a "description" from it's ...
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11 views

variational lower bound confusion

In this blog describing variational inference under the section KL divergence and ELBO they mention that in the equation $$p(x) = \frac{w(x)}{Z}$$ we can substitute $w$ and $Z$ with: $$Z = p(x;\...
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8 views

Loss function for a risk neutral binary classification

For binary classification task, with samples labeled $y=0$ and $y=1$, a neural network has one output node with sigmoid activation function, producing predictions $\hat{y}\in(0;1)$. Is the following ...
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1answer
14 views

How to interpret chart generated by gbm.perf function?

I'm new to GBM.Can you help me to understand the interpretation of gbm.perf function? I used following code in R best.iter = gbm.perf(train, method="cv") & got ...
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20 views

How to explain why KNN has good classification performance (have example)

I have a good prediction results on KNN-DTW group classification (group labels are either 0 or 1). But I don't know how to explain how discriminative the 2 groups are. Then I tried k-means and tsne ...
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1answer
13 views

Retuning hyperparameters of the baseline when comparing it with a new model

I have a baseline model which has certain hyperparameters to tune (it's actually a neural network, but I don't know if it's important in this context). I want to compare it with my own extension of ...
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1answer
24 views

Batch processing in a neural network

I am trying to understand how each batch is processed in a neural network. I understand that if we have a training set $X=\{x_1,...,x_{|X|}\}$ and we specify a batch size of $n$ than the neural ...
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34 views

What is the difference between KNN and K* algorithms? [on hold]

Are KNN and K* under the same category of machine learning algorithm? The explanation on KStar can be found on: http://weka.sourceforge.net/doc.dev/weka/classifiers/lazy/KStar.html Whereas KNN is a ...
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1answer
21 views

High AUC and Accuracy but weird output in confusion matrix

I am working on image classification problem to determine gender given a face. The dataset is located here gender face dataset on kaggle (link to my notebook). The class distribution is as follows. <...
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1answer
37 views

sklearn Support Vector Regression - test data prediction is constant

I am just getting into learning some basic machine learning for a project at university and I am having a little trouble with SVR on sklearn. When training a model I can change the epsilon value and ...
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1answer
26 views

Feedforward networks: methods to avoid two neurons in the same layer learning the same weights and biases?

There are many questions on this site which have to do with "what happens when two neurons have the same weights/biases" and I am not asking about that. However, it is occasionally the case that a ...
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19 views

PySpark ML: What to do when a logistic regression model is not generalizing?

I created a logistic regression model using PySpark ML. My feature set consists of both categorical and continuous features, and I ran the following to pre-process them: Categorical features: All of ...
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compare Bayesian linear regression vs standard linear regression

1st question, I recently learnt bayesian linear regression, but I'm confused that in what situation we should use bayesian linear regression, and when to use standard linear regression? What is the ...
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0answers
8 views

Learning useful semantic representations of data

Training a neural network on its final task (e.g. classification) right from the beginning is not always the best way to go. I'd like to make a short list of recognized methods of motivating a NN to ...
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1answer
43 views

Are the same number of trees required to compare Random Forest against GBM?

My training set has 13,737 observations with 53 predictors. I need to compare the accuracy of Random Forest vs. GBM. For Random Forest, I set ntree = 128 [based on ...
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1answer
16 views

Is the grid in a self organizing map static?

I'm trying to write my own SOM in python, and after reading material from several sources (and watching video tutorials) I think I understand all the steps. There is however one issue that I want to ...
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1answer
53 views

How to normalize data of 0s and 1s?

the data consists of one 2, so it's not binomial. is there any way to transform it to fit the normal assumptions? I tried square root and log, both didn't work
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12 views

Is there any formal explanation for the sensitivity of AdaBoost to outliers?

AdaBoost is known to be sensitive to outliers & noise. However, the explanation seems to be hard to found or nontrivial.
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11 views

Pointwise Mutual Information Word Dependency

I have pre-defined concepts which are either a single word or couple of words that refer to a concept.( In the context of machine learning for instance, covariance matrix is a concept). I am trying to ...
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2answers
660 views

Why there is square in MSE (mean squared error)?

Please forgive me for such a beginner question, since I'm learning stats . & machine learning. I'm trying to understand Mean Squared Error. I understand the "Mean Error", the Mean of Errors ...
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7 views

String similarity evaluation

I have been trying some String similarity functions such as Jaro Winkler, Cosine and a rule based one. I want to evaluate their performance. It is easy to get the True Positives and False ...
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1answer
22 views

Why does different factorisation matter in Markov networks?

I have been reading about Markov Networks that given some set of factors we can construct a unique graph G but not the other way around: "It should also be noticed that, given a set of factors, the ...
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0answers
14 views

How to derive the maximum *a posteriori* estimate when the prior distribution is Normal $N(m,r^2)$?

I am learning probabilities and I need a guru to help with this problem: Assume $p(y | x) = N(ax,\ s^2)$, where all quantities are scalars, $a$ and $s$ are known constants, and the prior ...
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13 views

Expected Classification error using indicator functions

I am given three variables (latter two are binary) $A,B$, and $C$, where $A$ = input vector, $B$ = whether data was chosen, and $C$ is the true label. $A$ and $B$ are conditionally independent given $...
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1answer
36 views

What does it mean to freeze or unfreeze a model?

I'm going over the fast.ai course on deep learning now, and there's frequent calls to freeze/unfreeze methods in the lesson ...
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0answers
19 views

Creating synthetic data from existing data

I have data with 8 features and 900 rows each. Those features are dependent on each other in some way. I want to conduct machine learning, but the data size is small. I was suggested to generate ...
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1answer
21 views

agnostic PAC model: Learnability and Bias-Complexity Trade-off

I am reading "Understanding Machine Learning: From Theory to Algorithms." In Chapter 5.2, it says that choosing the hypothesis class $\mathcal{H}$ to be a very rich class decreases the approximation ...
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0answers
12 views

sparse matrix in R [on hold]

I Try to compute a sparse matrix S which is generated by choosing a support set Ω of size k uniformly at random. S= P_Ω (E) , where E is a matrix with independent Bernoulli ±1 entries in Candes ...
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1answer
13 views

Wavenet joint probability

As presented in the first article of Google Wavenet (https://arxiv.org/pdf/1609.03499.pdf) the model can approximate the joint probability of the whole sequence (raw audio waveform) using the chain ...
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40 views

Machine-learning using an aggregation function

I am looking for a machine-learning algorithm (ANN, random forest,...) that is able to account for an aggregation function during the learning step. I want to estimate variable Y from variables X1,X2,...
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1answer
19 views

Predicting items based on time and weather

I've got a data-set with items bought, the time it was bought (I can add the weather of the location at that time of the day). I would like a simple "prediction" model based on time and weather. ...
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0answers
27 views

PCA, SMOTE and cross validation - how to combine them together?

I was reading a lot recently about PCA and cross validation and it seems that the majority call it malpractice to do PCA before cross validation. I would also like to perform SMOTE, but there is a ...
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0answers
40 views

Which machine learning technique suits this use case?

I have two different zones say for ex. IN and OUT and each zone with two features i.e., A & B. Now I have a target with the same two features and with the help of this, I need to identify, to ...
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0answers
27 views

Using a supervised learning to compare two conditions

I've got to analyze data about two signals x and y using machine learning but am stuck with how to proceed. Conditions are: 1) signal x and y are known to be linked to one another, but no parametric ...
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0answers
4 views

topic modelling and categorization of violations

I have a dataset where each row is a specific compliance violation. The first column is the name of the violation (Fire Exit, Aisle, Ergonomic Seats..up to 130 violations), the second column ...
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0answers
11 views

Conditioning the probability obtained from a machine learning model

I have developed a random forest classifier to predict whether a customer will churn. The data used to produce this model has the following form ...
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1answer
29 views

Offline evaluation of Counter factual data for Recommendation

I am building new model and facing at offline evaluation tasks. My goal is to predict higher CTR(=click/impression) advertisement, and improve sales.(sales would improve if user watch more ...
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1answer
32 views

Solve the optimization problem of tree, should we make each rectangle contains exactly one training data point?

I was reading the book "An Introduction to Statistical Learning with Applications in R". In page 306, when talking about the objective function of tree model, the book says: "The goal is to find ...
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0answers
11 views

Support Vector Machines conceptual question: Minimum memory needed to store parameters for linear and non-linear kernels? [on hold]

I saw a question asking what is the minimum possible memory needed to store parameters for a SVM classification model that outputs a boolean variable (so a binary classifier), where each data-point ...
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0answers
54 views

Do Stochastic Processes such as the Gaussian Process/Dirichlet Process have densities? If not, how can Bayes rule be applied to them?

The Dirichlet Pocess and Gaussian Process are often referred to as "distributions over functions" or "distributions over distributions". In that case, can I meaningfully talk about the density of a ...
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1answer
29 views

What's this class of algorithms called: (entire training dataset, new input) -> output?

Supervised machine learning algorithms normally work by preprocessing a training dataset and outputting a compact model (e.g. a bunch of regression coefficients) that can quickly give an approximate ...
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0answers
14 views

Soft Margin constraints in SVM

I have understood the constraints of Hard Margin SVM but stuck at Soft Margin SVM. The Objective Function along with constraints of Soft Margin SVM is given below. such that and The slack ...
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0answers
16 views

Skewness Impact on Classification

I have a dataset with 134 attributes and my goal is to build a binary classification model. While exploring the dataset, I found that there was high skewness present in the attributes. I wanted to ...
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0answers
47 views

What dose it mean for the model to return MTRY = 111? [on hold]

knowing that the data set has only 64 independent variable (Predictor). how that could be mapped to the number of variables used in splitting decision(mtry)! Is that a number of predictors = 111 or ...