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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13 views

Inverse Hyperbolic Sine Transformation (IHS) for dependent variable - How to back transform predictions?

I am doing IHS transformation for the dependent variable (count data, mostly 0 and small counts) while training a non-parametric tree-based machine learning model. I've seen posts saying it will ...
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1answer
146 views

Neural Networks - Strategies for problems with high Bayes error rate

I am building a Neural Network for a binary classification problem where the Bayes error (lowest possible error rate) is probably close to 50%. What makes the task easier is that I don't need to make ...
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2answers
118 views

Derivation of the conditional median for linear regression in “The elements of statistical learning ”

My question is about "The elements of statistical learning" book. I would like to know how to prove that the use of the $L_1$ loss $$L_1: E\bigg[|Y-f(X)|\bigg]$$ leads to have conditional median $\hat{...
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1answer
2k views

Understanding Add-1/Laplace smoothing with bigrams

I am working through an example of Add-1 smoothing in the context of NLP Say that there is the following corpus (start and end tokens included) ...
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1answer
43 views

How to find similar users in a social network

I have a set of users from a social network. These users are represented by large sparse vectors. Let's say that a small subset of those users bought a ticket for a particular movie. How could I find ...
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12 views

Using neural network with mnist cvs data format

I've been reading this book about neural networks, called "make your own neural network" and there he tests his neural network with data from MNIST, however the sample he is using is not the same as ...
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1answer
148 views

Autoencoders and/or PCA for highly sparse float vectors and a dataset of more than 2 million examples

I have a highly dimensional sparse dataset composed of 2.5 million of examples as follow : dataset_dimension=[2500000,360,280,18] Each example of this dataset ...
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29 views

Can anyone suggest the source to study basics needed for The Elements of Statistical Learning by Trevor Hastie Robert Tibshirani Jerome Friedman [duplicate]

anyone suggest the relevant study material to get the basics cleared for understanding the book The Elements of Statistical Learning by Trevor Hastie Robert Tibshirani Jerome Friedman
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3answers
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How to create ROC curve to assess the performance of regression models?

I knew that, ROC curve are use to assess the performance of classifiers. But is it possible to generate ROC curve for the regression model? If yes, How?
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14 views

Best approach to an event detection classifier model

Probably my title is not as precise as I'd like it to, but bear with me, the problem is quite straightforward: I have daily time-series sales and stock data, from January to May, both in 2018 and 2019....
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11 views

Retraining only fully connected layer

What kind of features fully connected layer have? For example using transfer learning if we just transfer the fc layer of target model to fc layer of source model and rest of model is assigned random ...
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1answer
329 views

Calculating alpha in EM / Baum-Welch algorithm for Hidden Markov

I am trying to use this equation to calculate the alpha (forward) probabilities for the EM/Baum-welch algorithm but I'm running into some confusion. I don't understand what the $h_t$ is. I know its a ...
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12 views

neural networks and optimization problems in general

Neural networks are efficient at solving optimization problems. The topic of optimization problems is divided into linear and nonlinear problems and in linear and nonlinear conditions. I just wonder ...
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1answer
18 views

Linear Quadratic Regulator

from http://people.eecs.berkeley.edu/~somil/Papers/lqrlecture.pdf Why must the matrices be positive semidefinite? What is the input authority cost? What is the purpose of multiplying the transpose ...
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4 views

Interpreting Local Outlier Factor (LOF) results

Using this example on the scikit-learn site, I am attempting to do some anomaly detection using LOF. What I end up with is this: ...
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0answers
16 views

How to decode a deep neural network as an analytical expression/formula? [on hold]

The question pretty much says it all. A shallow neural network is simple to do by hand but how to do it for a deep neural network (or is it possible without needing a mile long paper)? This becomes ...
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6 views

Model for simultaneous person detection and pose estimation

Does someone know a model that performs person detection (eg using a bounding box like YOLO or Mask-RCNN) and simultaneously pose estimation (like CPM or Personlab) in one forward pass. The models I ...
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1answer
124 views

Label smoothing formula

I recently came across this paper in section 3.2 it talks about label smoothing loss and how it's equivalent to s equivalent to adding the KL divergence between the uniform distribution u and the ...
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8 views

Gaussian process of a function derivative

Suppose that a function $f(\mathbb{R})\rightarrow\mathbb{R}$ $$ f \sim GP(0, k(x_i, x_j)), $$ is a zero mean Gaussian process with known kernel function $k(\cdot,\cdot)$. Suppose that the kernel $...
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7 views

What is the course structure of mathematics included in AI AND MACHINE LEARNING? [on hold]

I have just finished my basics in python....and automation....and I want to move Forward to AI amd machine learning. I'm a beginner. Please I need some help here!
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2answers
941 views

How do I find multiple change points in an online dataset?

I am trying to develop a Python based script connected to a SQLite3 database to identify distinct system changepoints in an "online" datastream. Changepoint must be identified in less than 2 minutes ...
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1answer
23 views

Understanding the process of transfer learning for NLP

Full Disclosure: I am a machine learning newbie. I have been learning about natural language processing for the past few weeks. To my understanding, the process of creating a supervised text model ...
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1answer
158 views

How to train a model when instead of a target we have a range where it is?

Often in machine learning we have a situation when target is numeric (real or integer). Each target comes with an associated input vector. The goal is to learn the mapping from the input vectors to ...
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19 views

Maximization bias in reinforcement learning

In Richard S. Sutton and Andrew G. Barto's book on reinforcement learning on page 156 it says: Maximization bias occurs when estimate the value function while taking max on it (that is what Q ...
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1answer
87 views

How to specify and fit a hybrid machine learning - linear model

I want to understand how some dependent variable y, depends on a known relationship with independent variable x, but also how <...
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1answer
159 views

Is there any Generative Model which can be used for Regression problems?

I've been researching Generative Models recently, and Probabilistic Graphical Models. Every time I read about Generative Models, I see they're trying to predict $P(x,y)$ or equivalently $P(x|y)$ and $...
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1answer
32 views

Higher RMSE but lower MAE and RMLSE. Which model is better? [duplicate]

I am evaluating two machine learning models. The output is count data which has a range of 0 to 30, which most of the output values being small values. Large output values are rare. One model has ...
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1answer
144 views

Sklearn imputing the mean issue regarding historical api and json rest api

I am trying to make a prediction on a new dataset via LendingClub's rest api. I also have historical data from them which I am using to create the model. I have split the data into train/test sets ...
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1answer
573 views

the role of basis functions in reinforcement learning

In the very simple examples of reinforcement learning (gridworld, mountain car), we use real numbers or some elementary functions as reward functions. When state spaces become larger and larger, and ...
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2answers
70 views

Pattern Recognition and Machine Learning (Bishop) - How is this log-evidence function maximized with respect to $\alpha$?

In the book Pattern Recognition and Machine Learning, the author writes the log-evidence function (equation 3.86 in page 167): ln $p(\textbf{t}| \alpha, \beta) = \frac{M}{2}$ ln $\alpha$ + $\frac{N}{...
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1answer
41 views

Why does feature scaling improves accuracy? [duplicate]

With feature scaling we just change representation of the data. This can make our model run faster but how this can improve accuracy? It is the same data after all. When I train my SVM without ...
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2answers
26 views

Is it possible to refit after cross-validation and continue to train the model for better accuracy?

A weird question below. Suppose you did a 10 fold cross-validation to show that a model is an unbiased estimator. And the results of the cross-validation also shows that training the model longer ...
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7 views

Log Likelihood Glmnet

I am not exactly sure if I understood the glmnet algorithm correctly (https://web.stanford.edu/~hastie/glmnet/glmnet_alpha.html). It says it uses a maximum likelihood approach to find a solution. ...
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22 views

I need to inject a scalar value on my pix2pix model but there is something wrong [on hold]

I am a newcomer in this forum so I don't know if anyone's ever asked that question before. I need help in my work: I want to implement a model that conditions a Generative Adversarial Network with an ...
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14 views

How to optimise 3-layered NN for regression predictions?

I'm trying to train a NN model on a regression dataset and trying to predict capacity. The size of dataset is 20773. My model is as: ...
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0answers
21 views

Generate synthetic data given AUC

I'm experimenting with ROC-AUC for binary classification problems. I want to generate synthetic data for a given AUC score. The ...
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1answer
612 views

Motivation behind parameter sharding for Downpour SGD

Why does the Downpour model shard the parameters into separate groups? Is there any advantage of making one cluster responsible for changing only certain parameters?
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2answers
10k 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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12 views

Can a binary classification model be used to classify samples on a probability scale?

Currently I have a machine learning model classifying samples into 4 groupings. But I am wondering if it is possible to make this a binary classification problem, using training data with only binary ...
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1answer
1k views

Condition for RNN vanishing gradients and eigenvalues of the matrix of weights

In this article on recurrent neural networks by Razvan Pascanu, $\mathbf x_t$ is the state at time $t;$ $\mathbf u_t$ the input at time $t$; and $\mathcal E$ is the cost function: A proof is given of ...
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0answers
46 views

Training data for extracted license plates from car images

I am working on a project which uses machine learning and image processing techniques to detect/extract license plates of a vehicle given an image. In my module for data preparation and feature ...
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3answers
36k views

Recurrent vs Recursive Neural Networks: Which is better for NLP?

There are Recurrent Neural Networks and Recursive Neural Networks. Both are usually denoted by the same acronym: RNN. According to Wikipedia, Recurrent NN are in fact Recursive NN, but I don't really ...
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1answer
1k views

Use matrix feature for machine learning or cluster analysis

I have a bunch of features that I would like to use for classification/machine learning and cluster analysis. Normally I use single point values or transformations of values for features and ...
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2answers
2k views

Word2Vec : Interpretation of Subtraction or addition of vectors

I am curious, what does subtracting vectors, as in [man – woman] do in regards to Google's word2vec calculation of analogy ? Is this a measure of how different the two vectors are? So is man – woman (...
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1answer
263 views

Bayes optimal decision for logistic regression: Self-study exercise

We want to find the Bayes optimal decision for logistic regression. That means that the goal is to find the actions, which minimize our expected loss (also often called expected cost or risk). Here ...
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9 views

Suggestion to use transfer learning in detection of Environmental norms violator [on hold]

I am working on one research, where the goal is to predict if a particular company will violate environmental norms in future from their websites screenshots. So, this goes to be binary ...
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1answer
144 views

Using a decision tree to predict a relevant location to a user

I am trying to create a decision tree to predict new locations a user would like to visit based on previous locations they have liked. Here is my problem, I have two data sets, a user data set ...
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2answers
33 views

Could anyone explain the terms “Hypothesis space” “sample space” “parameter space” "feature space in machine learning with one concrete example?

I am confused with these machine learning terms, and trying to distinguish them with one concrete example. for instance, use logistic regression to classify a bunch of cat images. assume there are 1,...
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0answers
10 views

Gradient Boosted Decision Trees How to Find Prediction of Each Tree? [on hold]

I'm doing a project. I have a classification problem that I should solve using gradient boosted decision trees. What I want to do is create a matrix that gives prediction of each decision tree for ...
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3answers
661 views

Combining one class classifiers to do multi-class classification

I am working on a 3-class classification problem. The classifier I'm using is Bayesian Networks which provides me with a classification accuracy of around 60%. When I do a two-class classification, I ...