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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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12 views

why scikit-learn is so disgustingly overfitting? [on hold]

I bumped into a problem that dazzles me very much, I can't believe it's actually happening. I trained an elastic net using last version of scikit-learn function ElasticNetCV and a 4 folds CV repeated ...
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1answer
9 views

similarity function for keywords that often occur together

I'm working on a project where I have to develop a machine learning solution. I should mention that I'm not very experienced in ML yet and that I'm not even sure if the term "similarity function" is ...
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0answers
17 views

Logistic Regression - Coefficients not defined because of singularities

I am running a regression model to predict dropout from an online program. People have to take 5 classes but some people dropped before taking the 5 courses. So I am using a dummy variables that is 1 ...
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0answers
10 views

Coding Random Forrest in R

I'm looking to code a random forest in R but am having a bit of trouble in my dataset. Before I get into the problem, let me reproduce my code below. The response variable of interest is '...
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1answer
126 views

How to prove mathematically that SBAF(Saha-Bora Activation Function) is mathematically linked to binary logistic function? [on hold]

How to prove mathematically that SBAF (Saha-Bora Activation Function) is mathematically linked to binary logistic function ? The activation function is as follows: $$y=\frac{1}{1+kx^\alpha(1-x)^{1-\...
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0answers
17 views

Understanding backprop equations

I was watching a video on backprop from deeplearning.ai where one particular thing confused me a lot. In the backprop, as shown below, Why aren't we averaging <...
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0answers
3 views

Approximate Bayesian Computation: Applications to Elevator Group Control Systems

I am working on a project where I will be using Approximate Bayesian Computation (Likelihood-Free Inference) in order to improve an Elevator Group Control System, e.g. minimize the waiting time of the ...
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2answers
4k views

K value vs Accuracy in KNN

am trying to learn KNN by working on Breast cancer dataset provided by UCI repository. The Total size of dataset is 699 with 9 continuous variables and 1 class variable. I tested my accuracy on cross-...
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0answers
12 views

How do you define the error in a hidden layer of a neural network?

I am reading some introductory texts on neural networks. While I am able to understand that the error in the final layer of the neural network is but I am not able to understand how the errors in ...
3
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1answer
51 views

How to plot logistic decision boundary?

I am running logistic regression on a small dataset which looks like this: After implementing gradient descent and the cost function, I am getting a 100% accuracy in the prediction stage, However I ...
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1answer
13 views

How to use convolutions of pictures instead of FC layers? [on hold]

How to use convolutions of pictures instead of FC layers? How can i do this effectively and efficiently.
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0answers
6 views

Having trouble figuring out how loss was calculated for SQuAD task in BERT paper

The BERT Paper https://arxiv.org/pdf/1810.04805.pdf Section 4.2 covers the SQuAD training. So from my understanding, there are two extra parameters trained, they are two vectors with the same ...
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2answers
30 views

Which tools should I learn to use in order to forecast sales for each day?

I am trying to forecast sales for a company that runs a few stores. In many cases, I am pretty successful using some basic methods in Excel to forecast sales for every month, but I'd like to be more ...
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0answers
7 views

Changing the regression problem to a classification problem

I'd like to do the classification on a crime data around the country. However, what I have for the label is the crime coefficient which is from 0 to 1. I'd like to make up some interval like 0~0.3 as ...
2
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1answer
59 views

Higher Order of Vectorization in Backpropagation in Neural Network

I am learning a machine learning class online from Stanford, namely CS 229. There is one section about deep learning and back-propagation in deep learning. The network looks like: The forward ...
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1answer
73 views

How to pre-process audio recordings for training a machine learning model?

Task: Process audio data so that it can be used for training a machine learning model--which would be used for labeling unseen/unheard audio recording in future. Data: The audio recordings are ...
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2answers
67 views

Variance/bias trade off regularisation penalty - why does it take this form?

In machine learning, if we estimate weights using a loss function $$L(W) = ||Y-F_W(X)||^2$$ (where $W$ is a weight matrix) we may add a "regularisation penalty" to control for the "variance/bias ...
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0answers
32 views

Bias Variance Decomposition 2.7 in Elements of Statistical Inference

I try to derive 2.7 from the book. I expose my demonstration $E_\tau[(y_0-\hat{y}_0)^2]=E_\tau[y_0^2]-2E_{\tau}[y_{0}\hat{y_{0}}]+E_{\tau}[\hat{y_{0}}^{2}]$ $= y_{...
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0answers
14 views

Deviance when y = 0

I am trying to compute deviance for the predictions of my dataset and I encounter quite a big problem here. Deviance is calculated as : $2 (\log(\mathrm{yTrue}) - \log(\mathrm{yPred}))$ where $\log$...
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0answers
5 views

How to get top features that contribute to anomalies in Isolation forest

I am using Isolation forest for anomaly detection on multidimensional data. The algorithm is detecting anomalous records with good accuracy. Apart from detecting anomalous records I also need to find ...
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0answers
6 views

Cost function increases dramatically at first then starts decreasing

I am training a simple linear regression algorithm using stochastic gradient descent. When plotting cost (MSE) vs number of iterations I get a plot which looks very strange: What would be the ...
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1answer
39 views

How do I implement stochastic gradient descent correctly?

I'm trying to implement stochastic gradient descent in MATLAB however I am not seeing any convergence. Mini-batch gradient descent worked as expected so I think that the cost function and gradient ...
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1answer
483 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
12 views

Are there any other image classification methods besides using neural networks?

When reading about image classification, the only occurring terms are "neural networks", "deep learning" and "CNN". It seems like there are no other methods for this task. I have worked with neural ...
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0answers
9 views

Normalising predictions across datasets

I am currently training a model to predict a binary attribute. The model gives the output in range [0, 1]. The metric is TPR@FPR, e.g. I need to achieve maximum ...
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2answers
32 views

How effective is SVM over big datasets?

I have a dataset of 800,000 observations and 11 features that I am using for a classification problem. I tried to optimize my model many times but in vain. The one thing I haven't tried is using SVM. ...
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0answers
6 views

Why different seeds produce different mse values for regression tree and ols?

I compare regression tree and ols in terms of out of sample prediction. I realized that the mse values changed when i change the seeds before getting train and test set. Sometimes ols is better ...
2
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1answer
812 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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0answers
13 views

XOR Neural Network, Problem finding shapes of delta for backpropagation algorithm

I am taking the Machine Learning course by Andrew Ng on coursera. I am trying to make a neural network learn to do XOR, but I am facing a problem regarding the shapes of the $\delta$ vectors, and $\...
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1answer
57 views

Local variable importance vs Global Variable Importance

Is there any technique to find local variable important? For example in credit card concept, is there any way to specify why a person is not eligible for a credit card? or which features cause this ...
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0answers
9 views

Best Machine Learning Algorithm for Grouping Similar Census/Survey [on hold]

I am working in a company which have many census/survey. The problem is there are so many census/survey that have similar questionnaire variable which made many of our census/survey seems to be ...
1
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2answers
34 views

Define attribute importance in unsupervised learning [on hold]

I'm using 'NbClust' package to help me to get the "optimal number of clusters" and I noticed in my dataset I have attributes with different importance. I have 5 attributes: x1,x2,x3,x4,x5 and I know ...
2
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1answer
54 views

Scaling data with different importance

I have 9 attributes: x1,x2,x3,x4,...,x9 and I know that the attributes x9 must have the same value in a cluster and the attribute X1 have more importance than others (x2,...,x8) I'm using Euclidean ...
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0answers
13 views

Selecting SVM parameters if training data is oversampled/undersampled

I am working on classification for highly imbalanced data. Let's say I have a strategy to oversample/undersample the training data. I plan to use an SVM classifier to perform the classification. Now, ...
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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 ...
1
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1answer
43 views

Regression on Auction Prices, Multiple Prices Randomness

I'm currently building a model to predict internet auction sale prices of products in a marketplace. There are a lot of instances where a product goes for multiple prices but it's basically the same ...
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0answers
13 views

Encoding quantitative outputs for regression

From Elements of Statistical Learning: For a two-class G, one approach is to denote the binary coded target as Y , and then treat it as a quantitative output. The predictions Yˆ will typically ...
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0answers
17 views

Training error higher than test error and validation error

I am training a genetic algorithm for classification and strangely, the training error is consistently HIGHER than the validation and test error. The training and validation set are both small size ...
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3answers
553 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 ...
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0answers
16 views

is backpropagation appropriate for training actor-critic when using Neural networks? [on hold]

I'm confused whether the backpropagation is appropriate to train the actor as well as the critic. If it possible I would like to know what is the update part for both. I used already to rain the ...
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1answer
20 views

When does OLS Regression outperform regression tree in term of out of sample prediction?

In my Master thesis i compare ols regression to regression tree to predict wages. I thought that i will get better prediction with the regression tree because it cathes more interactions. But now i ...
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0answers
15 views

Is it useful to add a proportion hyperparameter in the concatenation layer?

I'm reading a paper on deep learning-based recommender systems: Neural Collaborative Filtering. There are two sub-networks, GMF and MLP, which are fused into a unified model, by a concatenation layer. ...
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3answers
1k views

Linear Regression Model with Many Features - Real Life Example

I am learning Machine Learning (Linear Regression) from Prof. Andrew Ng's lecture. While listening when to use normal equation vs gradient descent, he says when our features number is very high (like $...
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0answers
28 views

How to derive a ranking function by analysing feature correlations

I am analysing some employee details to find the efficiency of the employees. Ideally I want some rankings to rank them based on these features. My features include; current salary projects ...
68
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7answers
23k views

Euclidean distance is usually not good for sparse data?

I have seen somewhere that classical distances (like Euclidean distance) become weakly discriminant when we have multidimensional and sparse data. Why? Do you have an example of two sparse data ...
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0answers
23 views

what is a vector perpendicular to a plane of vectors

I have 3 or 4 vectors connected that forms a plane. How can I find the vector that is perpendicular to this plane? it can be a unit vector as long as it preserves this direction. each vector is on ...
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0answers
117 views

Which GAN is the best for data augmentation?

I have around 200000 images and I want to augment the data by generating more of them. Images do not have classes, because they are the same object and are used for the task of object detection. Can I ...
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1answer
43 views

What's are the advantages and disadvantages of incremental learning?

Generally speaking, we all know it's to save spaces with incremental learning. According to the ques in stackoverflow , it also said that. But what's the disadvantages? What I know from my ...
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) ...
2
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1answer
12 views

Model to Recommend Ideal Parameter Changes for Best Performance of Industrial Machine

I'm trying to develop a machine learning model to solve this problem, and am unsure of where to start. We begin with some user-defined settings. The settings are used by a machine to create a product....