Questions tagged [machine-learning]

Machine Learning is a subfield of computer science that draws on elements from algorithmic analysis, computational statistics, mathematics, optimization, etc. It is mainly concerned with the use of data to construct models that have high predictive/forecasting ability. Topics include modeling building, applications, theory, etc.

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Can transformers be used to solve for a number of independent polynomial inequalities or polynomial equations?

I'm interested in solving constraint satisfaction problems involving polynomial functions of real variables using transformers. The papers available only deal with boolean SATs in CNF format e.g., ...
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Which technique should I use for the given scenario?

I've a list of numbers(say A) , I want to find out how closely related another list of numbers(say B) is to A. By close relation, I mean B should follow the same trend as A , I want to show the ...
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Should I normalise my data if future unseen data may have a different range?

I'm new to ML and researching data prep, more specifically feature normalisation. My question is whether it's a good idea to normalise data when its range may change over time? For example, if I'm ...
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How does L1 normalisation work in Binary Classification?

I was working on a project where I was using TF*IDF algorithm. After applying grid search, I got the tfidf_norm=l1. Can someone explain how L1 normalisation form works in binary classification?(I have ...
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How to train a neural network with symmetrical output?

I am attempting to train a neural network to predict output that is symmetric with respect to certain operations. It seems to me this creates a problem for training, because there are multiple equally ...
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Multiple timestamp values for training data

I am trying to extract features from audio WAV-files with the openl3 package. It is working so far, but since openl3 works with windows, I have now for each WAV-file two numpy files, one with the ...
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Treating recommender systems as multiclass classification or binary classification problem

I'm thinking about the two following approaches for building a recommender system to recommend products using implicit data as a classifier: Treat it as a multi-class classification problem. The ...
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1answer
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Heauristics for a NER model prediction

I am trying to build and NER model that can name entities in a "Job description." The entities are: Mandatory skills (Must have skills like java, python, c++ etc.) Nicetohave skills (the ...
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Types Of Plots for Discrete Data

So I have a lot of discrete variables in my dataset and want to visualize them (univariate for now). I went through various articles over the internet and it is suggested that histograms and count ...
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Applied ML course, how to access [closed]

I am a beginner towards learning Ml, and wanted to start out with the course of applied ML from appliedml.co. Is it the right choice, or would the Andrew Ng course from stanford be better. P.S - Also ...
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What is the purpose of defining such measure as metric or non-metric?

proximity measures can be metrics or non-metrics. the following criteria defines a metric dissimilarity measurement: here is for a metric similarity measurement I would like to know the consequences ...
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Yolov3 Tiny: What do each of the 2535 cells detect?

Source: https://towardsdatascience.com/yolo-v3-object-detection-53fb7d3bfe6b According to this image, it says the red grid is responsible for detecting the dog. Similarly, do other cells detect "...
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Reommender system model Predicting the time watched duration for each user_id-video_id pair

I just want to ask If I can use Surprise Library (SVD algorithm) in building a recommender system that predicts the watch duration for a user_id and video_id pair? I have a dataset that contains the ...
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Why do RNNs share weight?

If weights are not shared the number of parameters will be extremely large and difficult to compute which I understand. I don't understand the argument that varying length inputs are taken care of by ...
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1answer
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Multivariate time series (many to one relationship)

I am working on a machine learning project for my summer internship and was hoping someone could help. I am new to the field of ML and am still learning so please bear with my attempt for an ...
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Advantages of matrix factorization when the number of products is low

I'm building a recommender system where the number of products is rather low (around 50), and we can assume it'll stay the same for a long time. I'm looking at two different way of tackling the ...
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Recommender System with Time as the dependent variable and not ratings

I'm currently designing a recommender system in watching videos (all with same duration). I have the user_id and the video_id and I have the data for users's watch duration for the video_id. I ...
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I'm trying to use InceptionV3 as a pre-trained feature extractor for shorthand writing image captioning, am I on the right track?

I'm new to AI and I'm trying to understand image captioning for a project that I'm working on. I'm trying to build a translator for Gregg shorthand writing. I'd be feeding pictures of individual Gregg ...
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1answer
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Why checking the distribution of data is needed before calculating Gower distance?

I read this article(Clustering datasets having both numerical and categorical variables) to learn how to perform clustering on datasets with not just numerical variables. Before calculating the Gower ...
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Estimate best vector in one vocabulary based on input from another vocabulary

I have strings from two sources lets call them two different vocabularies maybe and I have an association defined between vectors of strings in vocab1 to vectors of strings in vocab2. ...
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1answer
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What is the typical things in Data that i have to look for, when implementing Survival Models using Machine Learning?

Problem Scenario I am working on an industry specific problem focussed on predicting the failure of a seal/gasket in the given time interval(T) in a high-pressure-compression environment. Whenever ...
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1answer
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Extracting Keywoards from messages with own NER Model

I'm starting a project where I want to extract keywoards from given messages. The keywoards are for example something like: "hard disk", "watch" or other technical components. I'm ...
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How to introduce a parameter for measuring change in data over time

In my project, I need to introduce a measure for 'movement' using a 3axis accelerometer (ADXL345). As sketched below: I thought about introducing some micro-changes, i.e. absolute change in ...
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Recommendation system or customer segmentation?

I have a dataset containing multiple tables including customer information, customer transactions and list of rewards campaign for customers. I am trying to figure out the customers to run a campaign ...
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1answer
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Does the abstraction of a class affects the performance of neural networks?

For example, if I have 3 audio classes including Ambulance Siren Police Car Siren Firetruck Siren assuming these 3 classes could be distinguished by humans. If I just want the model to classify all ...
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1answer
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Can landmark detection be only used for faces and human bodies?

I want to use landmark detection for finding specific points of interest in an indoor setting e.g. bedrooms, bathrooms etc. Is it possible to use it? So far I have only seen landmark detection being ...
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1answer
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What technologies or services should I use to generate text reviews based on neural networks? [closed]

There is a task. We have a huge database of reviews for certain brands / products. I need to generate meaningful review for a new product based on a trained model for this database. I can parse some ...
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Continuous Machine Learning with Log Streams

I am doing research in Continuous Machine Learning/ Life Long Learning. Two of the use cases I came across were Predicting Failures and Anomaly Detection using log stream data. However, already there ...
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1answer
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How to choose the right threshold for binary classification?

I am currently working on the titanic dataset from Kaggle. The data set is imbalanced with almost 61.5 % negative and 38.5 positive class. I divided my training dataset into 85% train and 15% ...
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how to prevent feedback loop while you can't use another data source

we have a harassment prediction model which trained and deployed many years ago. each day this model predicts some conversation as harassment and annotators label these predictions. (they only label ...
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Calculate following probabilities for the Bayesian Belief Network?

a. A fish caught in winter, in the South Atlantic, is a Salmon and is medium and wide. b. A fish caught in summer, in North Atlantic, is a Sea Bass and is medium and thin.
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What type of clustering algorithm to use to determine what accounts belong to a family?

I have both categorical (Name, address, etc.) and numerical data (similarity scores between two parameters) and I can't figure out what kind of clustering ML algorithm would be appropriate since most ...
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Hidden Markov models in Speech Recognition

My first question here. So I am trying to build a sign language translator(from signs to text) and noticed that the problem itself is quite similar to speech recognition, so I started to research ...
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Use probability estimate of logistic regression of a binary dependent variable: how to evaluate the model?

Logistic Regression gives a the probability for a specific outcome. It does not return the outcome itself. Say, we use the continous probabilities themselves instead of the binary classification for ...
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sklearn tools for assigning probabilities to the labels

I am using sklearn tools in a ML project. I have doubts about what I am doing. It is about a binary classification using 1 and 0 as labels. The first line of code ...
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What is the best approach for detecting anomalies in raw sales data using ML?

Assuming a data schema like below: sale_date Timestamp, store_id Int, cashier_id Int, customer_id Int I need to detect some specific anomalies: ...
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23 views

Linear regression to find differences between model performances

For one of my projects I needed to create classification models for each of many products. In order to see which classifier performs best, I created one SVM, RandomForest and Naive Bayes model for ...
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1answer
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AttributeError: 'DataFrame' object has no attribute 'data' [closed]

I am working with the iris dataset and I got the following error: AttributeError: 'DataFrame' object has no attribute 'data'. I'm working with iris dataset I would ...
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1answer
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How should a stateless data transformation be applied in regard to train/test split?

I want to apply spatial sign transformation to my data, but unlike other transformations this one is stateless. I am using sklearn and normallly i would first use ...
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Multi-step forecasts of factory production data using a Seq2Seq Encoder-Decoder Model with Attention

I am attempting to use a Seq2Seq model to make forecasts of factory production data using an Encoder-Decoder model augmented with Attention. I have become a little stuck with the shapes necessary ...
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Can one adapt LabelEncoder to your needs?

For a binary classification, I am using LabelEncoder. Let the two targets be A and Non-A. I want to assign the value 1 to A and 0 to Non-A. Unfortunately, ...
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1answer
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Anomaly detection using LSTM AutoEncoder

Having a sequence of 10 days of sensors events, and a true / false label, specifying if the sensor triggered an alert within the 10 days duration: sensor_id timestamp feature_1 feature_2 ...
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23 views

How to run PCA when data contains some categorial features?

Assume that we have a dataset with various features, and some of the features are categorial. And PCA dosn't work good on categorical features. How should I handle such datasets using PCA, what is ...
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Relation between KDE and Kernel Aproximation

I'm a bit confused. What's the relation between Kernel Density Estimation (KDE) and Kernel Aproximation in Scikit-learn? If there´s any? If not, what are their differences? Thanks.
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Recommender system for matching user input keywords to objects that have different keywords assigned to them (and getting the matching weights)

I'm looking for some tips in the right direction as to what to look into for this recommender system: We have a predefined set of objects, each with a few keywords assigned to them. We can call the ...
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1answer
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Can I set the rewards of a multi armed bandit problem with deterministic values?

I am new to reinforcement learning and I am tryng to understand the multi armed bandit problem. I think I have understood that it consists in choosing the bandit that maximizes the future reward. My ...
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Deep learning model is giving strange metrics

I am trying to build a deep learning model for automatic short answer scoring using TensorFlow. I am using this dataset: https://www.kaggle.com/c/asap-sas/data I am trying to build a model that suits ...
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What we can learn from the data if PCA scree plot bins are almost the same?

Suppose we have a data-set with 4 features. Suppose we calculate the PCA for this dataset and we plot the scree-plot: What we can learn from the features? Can we ...
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Anomaly Detection: LSTM Autoencoder Zero Reconstruction Loss on Anomalies

I am using an LSTM Autoencoder model for time series anomaly detection. None of the anomalies get flagged because the reconstruction loss comes out to be zero for all data points on the clearly ...
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1answer
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How should I implement machine learning for multi-tenant website?

The company I work for has a website for personal use to track leads and opportunities. I implemented a linear regression algorithm to predict a score for opportunities which is trained on the ...

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