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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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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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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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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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37 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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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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39 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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23 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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61 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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136 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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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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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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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
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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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73 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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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
40 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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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
49 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
67 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
36 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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38 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
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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
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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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47 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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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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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 ...
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1answer
23 views

Machine learning algorithm to classify matrices [closed]

I want to know what could be first choice for a machine learning algorithm to classify matrices. Each matrix belongs to either class A or class B. The classification problem is: To classify each ...
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1answer
35 views

Similar students using Machine Learning

I have a student performance data, where I have marks of various subjects for the students and I want to find similar students with good marks in a particular subject using machine learning. How do I ...
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1answer
24 views

Strategies for continuously assessing and improving model performance

I am building a supervised machine learning model to generate forecast. So I would have historic data like this: SKU, Month, .... other features, Actual Volume ...
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1answer
37 views

Performance of model in production varying greatly from train-test data

I was wondering if anyone has any advice on where to start digging for this problem. I have a model which has gone through development and all train/cv/test data sets now perform above 95% both for ...
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0answers
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Neural Network for detecting/checking for requirements in diagrams

My question is more about what approach is a good/the best approach for my problem: THE PROBLEM - I'm an (mechanical/software) engineer and we take extensive amount of time to review technical ...
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43 views

Improving automated ingestion system using Machine Learning and/or NLP

I'm working on a automated ingestion system which takes a PDF or doc file or a URL. It then parses the file and get me the required text in a json format but there are some error and there are few ...
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57 views

What are Supply Chain Management Problem?

Recently when going to data science hackathons, I have found that people mostly ask problem regarding Supply Chain Management problem. Can anybody explain to me what are these problem and how do I ...
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1answer
72 views

Why am I getting crossvalidation scores of 0 only

I am trying Catboost package with iris dataset with following code: ...
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Supervised training based Tagging/Labelling for entities

Let’s say I have training data tagged/labelled like this: ...
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1answer
49 views

Supervised learning for timeseries classification problem

I'm trying to use a supervised classification algorithm on a timeseries problem and my model is performing to well i think. It's a time to failure problem. I have 1000 sensors and have to predict if ...
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1answer
382 views

How to visualize image segmentation results

I am using u-net to do semantic segmentation for N>1 classes. The input size is (128,128,3), the output size will be (128,128,N). what is the correct way see the prediction as an image ot size n1 x n2 ...
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2answers
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Is there any tool for data visualization and manipulation?

I have a time series data set that I need to manually label them for supervised learning. What I am doing now is using excel to plot, and when I see the pattern that I want, I hover over the data on ...
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1answer
57 views

how to build a predictive model without training data neither historical data

I m trying to score "how much a product is expected in the market". I created some features: How much this product is used each year. Where was it used . how many product for each country. the main ...
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1answer
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Compare job ads with a given set of categories (which each consists of terms)

For a recent research paper, I plan to perform the following, for which I'd kindly ask for your advice. I obtained a set of a few thousand job ads. I now want to analyse how and whether these job ads ...
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2answers
70 views

Can preprocessing the whole population cause data leakage?

Introduction I understand the problem of data leakage that could be caused by the preprocessing step when our training and test sets are just samples of an unknown population. The preprocessing ...
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0answers
24 views

why does ID3 Decision tree algorithm not give the best decision tree?

I was going through ID3 algorithm, and what I believe is it incorporates Greedy Search rule to get come up with the decision tree. If it gives the best split possible at every stage, how does it not ...
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1answer
80 views

What are the differences between Reinforcement Learning (RL) and Supervised Learning?

What is the difference between Reinforcement Learning (RL) and Supervised Learning? Does RL hava more difficulty in finding a stable solution? Does Q-learning have more difficulty in finding a ...
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1answer
96 views

Detecting abnormal 'cat' behaviour via Supervised Learning

A few work colleagues and I were looking through a recently replaced 'cat', we had in the workplace. For those of you which are curious, the 'cat' in this context, refers to a specific type of pump ...
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1answer
596 views

Is max_depth in scikit the equivalent of pruning in decision trees?

I was analyzing the classifier created using a decision tree. There is a tuning parameter called max_depth in scikit's decision tree. Is this equivalent of pruning a decision tree? If not, how could I ...
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1answer
70 views

Which ML algorithm to use if we have categorical data, numeric data, derived data (derived from) other variable in our data set? [closed]

I am a beginner in Data Science. I have a data set which contains numerical data, categorical data and derived data (derived from other columns). The target column (dependent) is binary. Which Machine ...
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1answer
975 views

Why neural networks do not perform well on structured data?

I was recently working on some classification problem where decision trees performed better than neural networks. I had tried various combinations with neural networks altering the number of neurons / ...