Questions tagged [machine-learning-model]

A machine learning model is a simplified representation of a dataset, derived from statistics in the data, used to make predictions. It can represent patterns, behaviours or features within this dataset which have been learnt by the algorithm during training.

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40 views

What are the most known ML-models that use complex numbers? (if there are any)

Basically just the header. The question is out of curiosity as I haven't seen one yet.
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Best way to train a model having train/test/val set [closed]

I have a dataset structured as well: X0: the first part of my time series where there are no anomaly values. I have x0_train which is the train set, x0_test which is the test set and x0_val which is ...
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Do Machine Learning algorithms uses all the provided dataset to perform a model?

I believe that machine learning don't use all the dataset in performing a model, more specifically, it should have a part of the data that is a garbage set where the model ignore it or it doesn't help ...
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TSNE interpreration and separability

I have a binary classification problem where I train a neural network on a training and validation data sets. But I am not satisfied with the performance of my trained classifier (the NN above). The <...
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1answer
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Error during the compilation of a neural network in Vitis AI, "Not found op in super_const_dict: name: Decoder_Section_1_UpConv_1/kernel"

I'm following a Xilinx Tutorial about the implementation of a Neural Network in a System on Chip (ARM Processor + Xilinx FPGA) and I have come up with an error during the compilation step. I've ...
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Large dataset with large sparse and dense features - solve memory inefficiency

My model stores sparse and dense features during training. When operating on a large dataset (consider 10^5 samples), the memory requirements explodes during the training. What could be done to solve ...
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1answer
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How can I learn to better explain architectural choices?

I've found out that most of the choices made during model selection are based on a sort of trial and error. From what I've heard, even the most experienced Data Scientists cannot know beforehand ...
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9 views

Keyword extraction using NLP methods

I have some 100,000 keywords approx stored in elastic search and to extract keywords from a given text, i write a query to provide all the matching keywords. ...
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What AI predictions can be done using Temperature and Current features of electrical assets?

I have recently deployed hundreds of IoT based Temperature/Humidity and 3-phase Current sensors on electrical assets/machines(e.g. welding machine, integrated panel etc) in a warehouse and waiting for ...
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Predict future payments using Machine Learning [closed]

Considering a data set looking like the following, where the values correspond to the amount of money paid. ...
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2answers
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Which machine learning model is best for a combination of numerical and categorical data?

I want to develop a ML model which will allow my company to highlight employees which are at a risk of leaving the business, based on a variety of parameters such as performance, absence rates, ...
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Silhouette Score for different Clustering algorithms

I am trying to compare different clustering algorithms on a dataset and compare the model performance. Since the dataset is quite big (56 features), I applied PCA to reduce the number of features to ...
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1answer
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How can I model this problem to train a Machine learning model

I am new to Machine Learning and need help understanding how can I model the below (hypothetical) problem. Say I record the following info about thousands of people. For 365 days, each day I record ...
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1answer
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Two-level (large category and small category) label classification problem

At present, there is an app classification task, the input is the function description of the app, and the two labels are the major category to which the app belongs and the small categories under the ...
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average of two models with training set N/2 vs one model with training set N

I'm new to ML and I got a question about training model. Imagine linear regression $Y=\beta^TX+ \epsilon$ and we have training set D (size=N). I have two options: Train model use whole D and we get $\...
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What is the python code to find G-mean of classification models? [closed]

I am working on evaluation classification models for multiclass imbalanced problem.
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1answer
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How many layers of a pretrained model shoud be frozen?

I'm following an example of transfer learning where the blogger has frozen the first 20 layers of MobileNet. My question is , that is there any rule of thumb for how many layers should be frozen? ...
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1answer
24 views

Is standardization/normalization a good way of reducing the impact of outliers when I'm training a machine learning model?

Recently, I have read some papers in which the authors state that they have performed standardization/normalization of the variables for reducing the impact of outliers in the machine learning models ...
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Machine Learning Model Input and Output Flow

I am working on a backend for structuring and submitting data into a ML model. I have 3 questions regarding this process. What is the best method to feed the model continuous data (updated every 30m ...
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1answer
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What is the best Classification Method alternative to Nominal Logistic Regression, if your Response and all Predictor variables are Categorical?

Hy, I need help in choosing the best classification method. My response variable is nominal with "4" categories and five predictor variables, two of them are nominal and three are binary. ...
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How to perform feature selection on a dataset using correlation-based feature selection process

I have a dataset and on that, I have to perform feature selection using a correlation-based feature selection process (using scikit-learn), can anyone please show me how to do it with a small example ...
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1answer
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Assess the goodness of a ML generative model (text)

Take a RNN network fed with Shakespeare and generating Shakespeare-like text. Once a model seems mathematically fine, as can be assessed by observing its loss and accuracy over training epochs, how ...
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1answer
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What does "regularization" actually refer to?

I am familiar with regularization, where we add a penalty in our cost function to force the model to behave a certain way. But is this a definition of regularization? Typically we regularize to get a &...
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0answers
11 views

Using mathematical derivatives of input data to augment training input data

I'm thinking of how to design a basic feedforward neural network that would be able to predict future datapoints given past datapoints. I'm very new to neural network design so I'm wondering if there'...
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Forecast Model to Estimate Customer Service Call Volume and Appropriate Staff

I am working on a project to predict the proper staffing needed for a customer service team using historical data. I am new to machine learning, and I am not sure if my approach to this problem is the ...
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1answer
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How to write a Proof-Of-Concept(POC) for machine learning model?

I've found that If any company is interested in your product, But they don't know it will fit, it will work or they don't trust you, They will ask you for a POC or Proof-Of-concept I need to write a ...
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1answer
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Phrase/Token labeling

Looking for suggestions on how to define the following NLP problem and different ways in which it can be modeled to leverage machine learning. I believe there are multiple ways to model this problem. ...
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Keras model's embedding weight get NaN value

I am working on 3 categorical and 19 numerical features in which I plan to use trained embedding weights (from categorical features). After training, and get weights from embedding layers, I got NaN ...
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Building crop recommender system with past cultivation data not with ratings

I am planning to create a Crop recommendation system for farmers using the past ten years' crop cultivation data. it is a mobile application. whenever a farmer selects his location, the system will ...
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0answers
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Dense Keras network returns constant output, even for very simple models

I am trying to use keras dense neural networks to forecast some time series. When fitting my model on complex real datasets, my model converges toward a constant output, i.e. whatever the input, the ...
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how to determine the relationship between attributes/whether one has impact the other

I am trying to build a model that determines whether two products have an impact on each other's sales performance. Ideally, the result will provide me a ranking/score between each 2 product pairs. I ...
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How to estimate Coal Level in a Coal Train

I want to estimate coal level in each of the bogies in a moving coal Train. I want to get the percentage of how much it is filled. Can anyone please suggest me how should I proceed?
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pytorch lightning produces no checkpoint when learning rate fine tuning ison

My problem is concerning with using the automatic learning rate finder of pytorch lightning. In case I use this feature there isn't any checkpoint output produced at any time during the training of ...
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1answer
36 views

How to set the priority to Machine leaning algorithms for Binary classification among Four based on accuracy and fitting

Rain Classification in Australia Under this context, sklearn classification algorithms will be used, namely: Logistic Regression Classification (Parametric) Decision Tree Classification (Non ...
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1answer
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Why is my Neural Network having constant loss and always predicting a singular value?

I am trying to make a neural network on a dataset with 257 features and 1 target variable. My code looks like the following: ...
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0answers
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Features importance in model

I've been using azure's auto ML platform for a couple of weeks now and recently I've trained a model and came across a strange looking aggregate feature importance chart in the explanations tab. The ...
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How to perform nonlinear regression on data with error?

Most of of physical measurements are associated with error, I am wondering how to perform nonlinear regression in this situation. In the linear case, there are few methods like Deming Regression, ...
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1answer
24 views

Low P value in Chi-squared test but low coefficient in logistic regression

I ran a chi squared test on multiple features & also used these features to build a binary classifier using logistic regression. The feature which had the least p value (~0.1) had a low ...
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Categorical variables: create a risk class or include in the model?

I think this is a very basic question so sorry for the wordy format. I am trying to get my head around it. I am thinking about predicting earthquake damage to property in the US using a GLM algorithm. ...
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1answer
17 views

Encoding concept for categorical data - pick one for all the columns or different for different kinds in the same df

[Beginner here] If dataset contains - both ordinal, nonordinal (few categories) & nonordinal (multiple categories > 30). Is one supposed to pick one to encapsulate of all such situations or ...
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20 views

Should Chi Squared test for feature selection be applied on train dataset or the whole dataset?

I am working on building a logistic regression model. I am planning to run chi squared test for feature selection. Should I run it on train dataset or the whole dataset?
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1answer
28 views

In Text Classification if I get similar performance with 100 features and 200 features, which model should I go ahead with?

I have built two text classifier models, one has 200 features the other has 100 features (reduced to 100 from 200 after feature selection). I see similar performances in both. Which model should I go ...
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1answer
17 views

Shouldn't a test be repeated X times and average the results to determine the best machine learning model?

I have searched in several web pages how to choose the best machine learning model for a dataset and they all seem to agree that they should be compared using the same seed. However, they only run the ...
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21 views

Can there be scenarios where an overfitted model in machine learning cannot be generalized?

Is it always possible to generalize an overfitted model? I know there are ways to handle overfitting, but can there be scenarios where overfitting cannot be handled in machine learning?
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Rule of Thumb for number of observations required to train a model with n independent variables?

I am aware adding more features to a model leads to overfitting of a model. Is there a rule of thumb for minimum number of rows required to build a model with n features in order to build a ...
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1answer
22 views

How to use new dataset on a pretrained neural network model?

I have built a dataset that I would like to pass to a pretrained model in oder to perform some predictions. I am looking for some steps/processes to guide me in this. Should I fine tune?If so what ...
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0answers
10 views

Weight for Samples on SVM

there is a option sample_weight in fit(X[, y, sample_weight]) function (OneClassSVM, sklearn library). If I use the option ...
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1answer
36 views

How can I weight each point in one-class SVM?

I want to give weights to some data points Specifically, these are points related to anomalies (I'm implementing one-class SVM for anomaly detection) Exactly, I want to consider some data points that ...
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1answer
11 views

Measure performance of classification model for training on different snapshots

I am trying to do binary classification on some chronological data. Let's assume we have weekly data from the first week of 2017 through the last week of 2020. Now we have found out that 26 weeks of ...
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97 views

Why am I getting a different answer in Principal Component Analysis dimensional reduction?

Problem-: Consider the two dimensional patterns (2, 1), (3, 5), (4, 3), (5, 6), (6, 7), (7, 8). Compute the principal component using PCA Algorithm. Use PCA Algorithm to transform the pattern (2, 1) ...

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