Questions tagged [supervised-learning]

Supervised learning is a type of machine learning algorithm that learns a mapping function y = f(x) between input variables (x) and output variables (y). The two most common supervised learning tasks are classification and regression.

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12 views

svm.LinearSVC: larger max_iter number doesn't always increase the accuracy/precision/recall

Background: Supervised machine learning Data shape 10+ features, target = 1 or 0 only, 100,000+ samples (so should be no issue of over-sampling) 80% training, 20% testing train_test_split(X_train, ...
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8 views

Using Unsupervised / Supervised Learning for RGB manipulation

I need to teach a model(or any substitute) for automatically adjusting the concentration of R, G, and B values until they make up white colour. i.e, tweak the RGB values (beginning from some arbitrary ...
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20 views

Predicting probability for each tag given already chosen tags

I have a set of tags (~10'000, will be extended over time) presented to a user. After he has selected 3 or more tags, I want to predict for each remaining tag what the chances are that the user will ...
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16 views

Very simple real-valued time-series dataset for RNN prototyping

Is there a simple real-valued time-series dataset on which a vanilla RNN model can be trained. With "very simple" I mean only two to four real-valued inputs per time step and a single real-valued ...
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13 views

Is there a way to use Plotly as an annotation tool, for labelling time-series for instance?

I have been tasked to create a tool aimed at labelling sections and/or precise data points of a biomedical time-series. Our main framework is written in Python. I would like to know whether it is ...
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17 views

Algorithm for user profiling without distinct profiles

I am trying to design an algorithm that takes in a new user with the variables department, location, job_role etc. and I want a machine-learning algorithm to decide ...
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3answers
24 views

How do ML model retain/store the learning(s)?

In other words, if the model after training and testing is ready for making future predictions, it must be storing the learning(s) somewhere in memory or disk or cache (or I really don't know). So, ...
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1answer
63 views

XGBOOST : model.predict_proba() and model.predict() conflicting behaviour

I have two classes : 1 and 2 The output of model.predict_proba() -> [0.333,0.6667] The output of model.predict() -> 1 This is happening for around 200 test values out of the test data of 10 lac. ...
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39 views

Super Resolution CNN vs Regular CNN

I am digging into finding a solution for background subtraction and one of the requirements is to not loose in quality of input image. Found that there is a specific type of CNN like Super Resolution ...
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22 views

suitable algorithms for very high dimensional ”binary pattern" recognition?

I have a dataset consisting of 3000 binary features and one binary ouput. subset of these binary features form binary patterns. these subsets could be neighbouring features or from different regions (...
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34 views

Which feature to use in feature selection?

Objective: Multiclass classification with supervised learning, small dataset (25h) Context: My dataset is composed of mobile network data collected with a smartphone. The labels correspond to the ...
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44 views

Supervised Learning Quiz website?

What are some good websites which provide quiz questions on supervised learning and machine learning in general? I have a quiz coming up and I would like to be prepared for it.
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Why does feature scaling improve the convergence speed for gradient descent?

From this article, it says: We can speed up gradient descent by scaling. This is because θ will descend quickly on small ranges and slowly on large ranges, and so will oscillate inefficiently down ...
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Extract data from mainly unstructured sets and derive risk metrics out of those

I have the following question (this was a real life example): Q: Extract data from mainly unstructured sets and derive risk metrics out of those. From what you know or imagine about the data ...
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17 views

How to use fresh data when target prediction period is long?

I'm using supervised learning on monthly activity data to predict when a customer buys a particular product. This product is typically bought infrequently and at the moment my target variable is ...
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1answer
35 views

Is there an algorithm for categorizing unlabeled samples into K classes? [closed]

I am not sure if this would be considered unsupervised, or semi-supervised learning. I am looking for an algorithm that will take N input arrays of features, and then cluster samples(not features) ...
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2answers
34 views

Supervised learning approach - creating my own labels

Scenario - I have data that does not have labels but I can create a function to label the data based on behavior and deploy the model so I don't have to keep labeling the data. Is this considered ...
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1answer
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How to efficiently iterate a supervised model over the Cartesian product of very large number of records?

The problem: Two large databases, with ~1M records each, "old customer data" and "new customer data". The data came from different sources and was ingested at different times, so there are many ...
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1answer
30 views

Is conditional GAN supervised learning?

I am trying to understand this paper about conditional GAN, it says that extra information y (class labels) is given to the network. However, I cannot understand its usage during training or its ...
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1answer
33 views

Methodology for driving score(behavior)

I am an intern at mobility data company and a Master's candidate in Statistics. I am researching about driving score which is based on a driver's driving habit. We have trip data which contains the ...
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17 views

Which metric should be used to select best binary pixel-wise classifier for segmentation task?

I am doing a semantic segmentation task using a supervised algorithm to classify image pixels into one class or the other (binary classification). I am trying several classifiers and feature ...
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3answers
103 views

How can Clustering (Unsupervised Learning) be used to improve the accuracy of Linear Regression model (Supervised Learning)

I came through this questions and I failed to find the right answer for it. How can Clustering (Unsupervised Learning) be used to improve the accuracy of Linear Regression model (Supervised Learning)?...
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21 views

Proportion of positive/negative label in Supervised Learning

I'm working on a Supervised Machine Learning problem and I have a question about the proportion of positive/negative label. I would like to categorize some batch as OK or NOK. But actually my batchs ...
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semi supervised learning using transfer learning and shared memory

I am reading a paper here and I am not sure I am understanding something. They claim to have 83% unsupervised on CIFAR 10, but they used something that is semi supervised. At the very least, they used ...
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When is the sum of models the model of the sum?

The response variable in a regression problem, $Y$, is modeled using a data matrix $X$. In notation, this means: $Y$ ~ $X$ However, $Y$ can be separated out into different components that can be ...
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42 views

What kind of learning in this training situation when anomaly detection? Supervised learning,semi-supervised learning or unsupervised learning?

I am doing anomaly detection recently, one of the methods is using AEs model to learn the pattern of normal samples. Determine it as an abnormal sample if it doesn’t match the pattern of normal ...
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73 views

How to label a dataset for Machine Learning?

I have a collection of educational dataset. The dataset consists of a username and their review for the course. I want to analyze the data for sentiment analysis. How can I label the data to train ...
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21 views

Unsupervised clustering improved with supervised classification accuracy

I have a set of labeled samples each containing up to 300 different objects. For every object I have a set of features describing the object. For example, Sample with label '1': 50 objects of type ...
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1answer
53 views

Liner regression and feature scaling

Below are few questions where I unable to find out where I am wrong. I added screen shot of image and explanations of the each options that I am understanding. Questions are purely discussion based ...
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1answer
24 views

Balanced vs total dataset rows, which one is better?

I work on a dataset concerning games playing results. i.e every child play an indefinite number of games and it has as output (y) two possible values "success" or "Failure". It's about 800 000 ...
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62 views

Philosophical question on redundancy

Suppose I implement a supervised learning version of LSTM similar to this. Namely, I have these univariate time series data: ...
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145 views

which metric is better for boosting methods

I work on a dataset of 300 000 samples and I try to make a comparison between logistic regression (with gradients descent) and a LightBoost for binary classification in order to choose the better one. ...
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2answers
30 views

Labeling classes conditionally

I am working with a time series predicting whether web traffic will increase or decrease each day compared to the previous day for a given user. Initially I used binary classes: labeled 1 for next ...
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1answer
28 views

Supervised learning for a turn-based game?

So I have 4GB of turn-by-turn data for many games of a particular strategy game. It appears that most people interested in using ML to build an AI for turn-based games use reinforcement learning to ...
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67 views

Which algorithm to use for efficient resource assignment?

I am a starter in ML. So pardon me if the question is naive. We have a Project Management tool where users can create a ticket and assign it to others. This is just one part of the tool but we are ...
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40 views

Test RMSE of polynomial regression drops when using more variables?

I am testing polynomial regression for a data set of 50 variables and a sample size of 5000. I ordered the coefficients of the linear model from high to low and then made different models using the p ...
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1answer
192 views

What happens if GBM parameters (e.g., learning rate) vary as the training progresses?

In neural networks there is an idea of a "learning rate schedule" which changes the learning rate as training progresses. This made me ask the question, what would be the impact of varying ...
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1answer
85 views

How to approach a machine learning problem? [closed]

I'm a beginner in machine learning, and no real statistical background ( just basic knowledge ). I comprehend half of what is said on forums about statistical methods and techniques for normalizing ...
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24 views

Best model for Antimicrobial Resistance (AMR) prediction?

Some classes of problem are best solved by a specific class of machine learning model, due to the structure of the data (e.g. Deep Learning for computer vision). Prediction of bacterial resistance/...
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1answer
43 views

What algorithm to use

I am stuck on what algorithm to use. I want to train my program on a dataset where i have an input image, and an output image which is a modified version of the input. The whole context of the image ...
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29 views

Modelling a startup's funding journey with Brownian Motion

I am trying to implement a "light" version of a paper (Hunter, Saini & Saman 2017), in which the authors build a model capable of predicting the probability that a startup will exit (either by ...
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1answer
141 views

How does the cost function of LSTM works? [closed]

I am searching to understand how does LSTM network work, but I couldn't find any good sources that explains how it's cost function works? I mean I know we have a sequence of inputs ...
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1answer
87 views

ML regression poor performance

I am experimenting with 3 years time series electrical demand data (kW) for a building and attempting to create regression supervised ML models from sci kit learn regressor algorithms but I have very ...
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1answer
57 views

Unsupervised Learning and Training Data

As far as I know, we need to use training data to find out the relation between the features, also known as input values, and labels, that are output values, in supervised learning. After that, by ...
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57 views

Hyperparameter optimization when calculating learning curves

I'm selecting a model for a regression problem and want to calculate learning curves. My dataset consists of ~20,000 x-y pairs. I'm using kernel ridge regression with different kernels, different ...
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1answer
32 views

Perceptron Learning Rule

I am new to Machine Learning and Data Science. By spending some time online, I was able to understand the perceptron learning rule fairly well. But I am still clueless about how to apply it to a set ...
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1answer
22 views

Supervised or unsupervised learning for predicting energy consumption for new buildings

I’m working on an model for auto dimensioning district heating pipes for new district heating areas (new customers). I have energy consumption data on hourly basis and describe data about these ...
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80 views

How to choose best model checkpoint when training deep learning model on all the data?

When training a final model for production, it's often recommended to train on all available data (train + dev + test), as discussed here. I'm training a deep learning model. I typically save and use ...
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18 views

SVM Cost function change to improve its computational efficiency

While listening to Andrew Ng's course of Machine Learning he said that the SVM's cost function term $\frac{\Theta^T\Theta}{2}$ is usually changed to $\frac{\Theta^TM\Theta}{2}$, where matrix $M$ ...
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1answer
52 views

Apply Labeled LDA on large data

I'm using a dataset contains about 1.5M document. Each document comes with some keywords describing the topics of this document(Thus multi-labelled). Each document belongs to some authors(not just one ...