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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1answer
12 views

In sklearn, it seems that `dot(x, x)` corresponds to `np.sum(X*X,axis=1)[:, np.newaxis]`, why is that?

In a 2d Euclidean space, Let point $a=(a_x, a_y)=(1,1)$; Let point $b=(b_x, b_y)=(5,1)$; Let point $c=(c_x, c_y)=(4,4)$; the squared Euclidean distance between point a and point b is equal to $(...
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1answer
10 views

Why the dot product of two vectors in sklearn is not a scalar?

In a 2d Euclidean space, Let point $a=(a_x, a_y)=(1,1)$; Let point $b=(b_x, b_y)=(5,1)$; Let point $c=(c_x, c_y)=(4,4)$; the squared Euclidean distance between point a and point b is equal to $(...
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1answer
233 views

Target encoding a categorical variable in a highly imbalanced dataset for binary classification

I have a categorical variable, Industry, that has different values in a dataset that is over 400K datapoints. This dataset is highly imbalanced, the ratio of roughly 99/1. What I am doing is ...
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13 views

In this concrete example of applying sklearn knn (with kd_tree) on Iris Data Set, how many partitions are there?

The k-nearest neighbors algorithm (k-NN) is a non-parametric method used for classification and regression. In both cases, the input consists of the k closest training examples in the ...
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1answer
146 views

Best model to predict if the client will buy our product or not

I would like to find a good model to predict which client will buy my product in 2018. I would like to have opinions on which method can fit my data to predict which client will the product A in 2018. ...
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21 views

How can you measure probability with a regression model? [on hold]

Classifier-based machine learning models have a corresponding "confidence" associated with each prediction. How can you get a confidence measure for a regression model?
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1answer
21 views

What are some of the most correct/accepted ways to tune and compare different models in an academic context?

Those days, I have been reviewing different academic papers which mainly compare the performance of different machine learning methods on a particular problem. And I was surprised by the variety of ...
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1answer
19 views

How do you evaluate/test accuracy of Text-to-Speech (TTS) models?

As the title implies... For instance, for Machine Translation, we have BLEU. For categorization, we have categorical crossentropy, for binary categorization, we have binary crossentropy, etc. etc. ...
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1answer
14 views

hyper parameter tuning AFTER Nested cross-validation

I have read very well the awesome answers and suggestions by @cbeleites and @Dikran Marsupial here for nested CV but I am still confused about something: Basically now I understand that nested CV is ...
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3answers
304 views

Is it possible to perform a regression where you have an unknown / unknowable feature variable?

Is it possible to perform a regression where you have an unknown / unknowable feature variable? Say I have $y_n = a_0 + a_1 x_1 + a_2 x_2 + a_3 x_3$ but I do not / cannot measure the value of the ...
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1answer
15 views

When use CTC-loss for speech recognition?

I'm trying to understand and implement CTC-loss for speech recognition (here on SO). I'll like to have more information about the use cases of this technique. From what i understood, it is more ...
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1answer
353 views

Trending time series data normalization for Deep Learning

I'm replicating following article Financial Time Series Prediction using Deep Learning and I'm stuck with data normalization. In chapter 5.1 in the second paragraph in the last sentense the authors ...
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1answer
9 views

How do i re-train a final model after using oversampling?

I am a bit puzzled about the process of experimenting with a model and oversampling and then translating it to the final version of the model that will be used: I oversample the data (only the ...
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6answers
33k views

Why use gradient descent for linear regression, when a closed-form math solution is available?

I am taking the Machine Learning courses online and learnt about Gradient Descent for calculating the optimal values in the hypothesis. h(x) = B0 + B1X why we ...
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0answers
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“row” and “column” are the names of axes of 2d array, is there a similar naming for a 3d array?

row and column are the names of axes of 2d array. this python array, array([[0, 1, 2], [3, 4, 5], [6, 7, 8]]) could be viewed as a matrix that ...
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18 views

What are some reasons that a regression perfectly fits a test set? [on hold]

I recently built a simple linear model that I trained using a standard 30-70 split on my data set. To my surprise, when I tested my model on unseen data, it reported the following: With a linear ...
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1answer
540 views

What is the difference between C and lambda, in terms of the SVM?

I don't understand the difference between the parameter $C$ and $λ$ in terms of the SVM. It seems to me that they are both involved in regulating over-fitting of the data. What difference between $C$ ...
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0answers
10 views

How to visualize two-class linear discriminant analysis with multiple attributes

For a two-class linear discriminant analysis problem. If each class has only two properties, I can easily use these two properties as the x-axis and y-axis of the Cartesian coordinate system, using a ...
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2answers
6k views

Using Non-numeric Features

I'm just starting out with machine learning. The example I was shown during a mini course I took was the predicting of the sale price of a house given features like: size of house number of floors ...
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2answers
148 views

How to set up a linear system to interpolate the train data perfectly with Gradient Descent?

Consider a (consistent) regression problem (i.e. we are trying to predict a real valued function and we don't have inconsistencies in the way we map x's to y). I am trying to perfectly fit/...
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0answers
6 views

Constraint on ALPHA in Dual of L2 SVM Problem [on hold]

In the Dual of the L2 SVM problem, what is the Dual function and the constraints on ALPHA? where ALPHA is the Lagrangian Multiplier or the dual variable.
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0answers
12 views

SMOTE and Lagged Observations

I'm doing a project about the effect of synthetic oversampling in a machine learning context (more precise SMOTE for the oversampling of the minority class of a highly imbalanced target variable). The ...
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0answers
15 views

Weighted averaging of multi-task (multi-output) regression errors

I am trying to elaborate a multi-task (multi-output) regression metric based on single-task metrics. From my perspective, it should be a weighted average of single-task errors estimated with the same ...
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1answer
13 views

How to Predict the sales of all the items, offered in all the countries

I am working on a task to predict the sales of all the items offered in all the countries. The sales are aggregated on a daily and country level. Each Item has a history of past sales and prices for a ...
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1answer
19 views
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26 views

To what extend do the pitfalls of linear / logistic regression apply to other machine learning methods?

During my university days, they took great care to go through everything we could do wrong when using simple regression models. Reverse causality, omitted variable bias, heteroskedasticity, normality ...
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1answer
484 views

Find-S algorithm with 'false' in final hypothesis

I have this example in my book for the find-S algorithm: When we start the find-S algorithm we have first the specific hypothesis: ...
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0answers
17 views

mathematical simulation of svm workings

i cant seem to work my head around the mathematical part of svm i understand the concept and the derivations but the the part that comes after lagrangian formulation is where im stuck..(googling didn'...
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0answers
12 views

Regression with a growing year over year dataset

I created a hierarchical GAM to model final event sales as a function of sales to date, days until event, teams that are playing, and event month with a grouping at the home team level. This yielded ...
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0answers
4 views

H2o interpretability how to parallelize

I have trained a model using autoML everything is ok. But this model is for healthcare therefore is very important the interpretability so I have used "iml" Package in combination with the ML model ...
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1answer
287 views

H2O GBM and Caret GBM

Hi I have doubt regarding the interaction. depth parameter in caret. I found a useful link hereabout interaction.depth in caret Now I am trying to find the similar parameter in H2O-GBM . Can anyone ...
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0answers
26 views

How did these researchers determine the confidence interval of the AUROC using resampling but without retraining the model?

In this Nature article backed by Google, the investigators develop then externally validate a deep learning model for predicting lung cancer using CT scans. In their internal validation results, we ...
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10 views

Model recalibration in R for machine learning models [on hold]

I would like to recalibrate my model using R. I tried Frank Harrell's rms package but this one does not allow statistical or machine learning approaches. How can I recalibrate my model using ...
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0answers
31 views

Deep Learning for small 1-dim Datasets

I am trying to find a neural network architecture for a dataset (150 instances) with 10 features (numerical). The features are not associated to each other, so 1d-convolutions are not an option. ...
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1answer
949 views

Logistic Regression with gradient decent: Proper implementation

So after going through some machine learning courses, I tried to implement my own logistic regression, just to get a feel of it. My code: ...
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3answers
133 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{...
2
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1answer
646 views

NLP: How to do feature normalisation for gender classificiation?

NOTE Before I begin, this F-measure is not related to precision and recall, and its title and definition is taken from this paper. I have a feature known as the F-measure, which is used to measure ...
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1answer
25 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 ...
4
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1answer
148 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 ...
3
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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
48 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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0answers
16 views

Using neural network with mnist cvs data format [on hold]

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
150 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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0answers
31 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
11k views

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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0answers
22 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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0answers
25 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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0answers
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
19 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 ...