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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Error while using saved logistic regression model on scoring vector data -The columns of A don't match the number of elements of x. A: 6011, x: 232964
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I'm getting error while using saved logistic regression model on scoring vector data.
SparkException: [FAILED_EXECUTE_UDF] Failed to execute user defined function (ProbabilisticClassificationModel$$...
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Why do we need hyperparameter tuning in Scikit learn? Doesn't sk learn models by default give best model?
When I have the option to build a classifier like this directly
clf = RandomForestClassifier()
why do we perform tuning by restricting the parameters like this
<...
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Clustering words with similar meanings
What methods are there to cluster words/word phrases with similar meanings together from a list of words/word phrases?
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overfitting or not
Hello so i'm building a classification model i train my on various models and these are the metrices so i want to know if ther's an overfitting or not
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How to build a categorization system without a target variable?
The data
I have a large dataset containing execution logs from various tests conducted over several years. The logs can be noisy and often contain a plethora of messages detailing the ongoing ...
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ML paper reproducibility
How can I reproduce results in an ML paper if I don't have the identical resources to train the models as in the paper ? (in my case I only have a laptop spec NVidia gpu and in most of the papers I ...
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Expanding Training Data for Intent and Entity Recognition Model
I have a specific use case where I need to identify both intent and entities within a given statement. For example, given the statement "Book train tickets from Mumbai to Delhi," the intent ...
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How Can I Train a Real-World-Ready Classifier with Limited Real Data and Abundant Open-Source Data?
I am trying to train a text classifier with open-source data to generalize on the real user traffic (henceforth "real data"). However, even though I have many annotated open-source data, I ...
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Optimizing decision tree
I have a question regarding the technique/technology which could be applied for the issue:
Suppose I have a rule-based tree or decision tree which predicts a variable Y based on variables A,B,C. This ...
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Utilizing Colab's GPU/TPU to Speed Up General Python Code not just data science code
I am new to using Colab and trying to speed up my code by utilizing the GPU/TPU runtimes. I have a block of plain Python code without any data science libraries like TensorFlow or PyTorch. I was able ...
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Understanding Multi-headed Attention from architecture details
I've a conceptual question
BERT-base has a dimension of 768 for query, key and value and 12 heads (Hidden dimension=768, number of heads=12). The same is conveyed if we see the BERT-base architecture
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Why is it difficult to use a linear regression model for the classification problems?
Why is it difficult to use a linear regression model for the classification problems?
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LSTM - How can I predict the status an hour before in advance?
I’m very beginner, I’m trying to design a prediction model for forecasting the status one hour ahead.I have 150 sample data, each consisting of of 24 hours of time-series data with multiple features (...
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Best practices on encoding on an increasing number of categorical variables
I'm currently using Gradient Boosting Regressor as my model to predict production risk based off a set number of features as a side-project. One of these features, ...
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What does "explicit latent variable optimization" mean?
The paper MotionLM: Multi-Agent Motion Forecasting as Language Modeling states:
Our model, MotionLM, provides several advantages: First, it does not require anchors or explicit latent variable ...
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What're the standard ways of padding data for GNNs?
I am working on materials property prediction using GNNs with torch_geometric.
Each data in my dataset has different number of feature vectors x, edge_index vectors ...
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Tensorflow outputs nan for basic object detection/classification
I am receiving nan as my accuracy and loss outputs after each epoch for basic object detection in tensorflow. Also, my results (classification and bounding box ...
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Semantic Scoring and readability for short sentences
I am working on short sentences for NLP based classification. I wish to make a assessment if a sentence is readable before training the system on it. Now readability scores are not working since ...
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SMOTE-NC not working, Error: Pandas output does not support sparse data
I want to get my SMOTENC to work, but i've been failing successfully
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The optimal way to stratify a numerical target variable into a categorical one for a machine learning algorithm
I have tabular data, the predictive variables are numerical and categorical and the target variable is a numerical one. Using the proper techniques I can make predictive models with R^2=0.95.
Now let'...
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How is it called when instead of creating predective models finding patterns in observed data (ML) you tried to guess the model theorically...?
I'm a college student appasionated of machine learning and I've decided to my bachelor thesis about it. I thought that as an interesting introduction to machine learning, I could introduce it by ...
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How to input or estimate missing text data?
My task requires 32 different columns with 25 beeing independent text data.
Deleting nan values, or cutting columns which has less then 20% (of non NaNs) results in reducing dataset to less then 10% ...
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Is ALE curve value constant for observations in the same interval?
I have been reading Chapter 8.2 of the Interpretable Machine Learning about Accumulated Local Effects (ALE) plots. It includes the following formula which defines the ALE value for a given value of ...
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What is the best sampling strategy for correlation analysis?
I have a big dataset, and i want to finds subspaces with high correlation among features. I want to take only samples of data. So, what is the best sampling strategy in this context.
Thanks
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In the GAN objective function, why do we first do we first find the D(x) that maximizes the objective function and then maximise wrt the generator?
The GAN objective function is optimised like this: argmin(argmax(L(G,D))) where the argmax finds the D (Discriminator) that maximises L(G,D). Why is it not the other way around, i.e. argmax(argmin(L(G,...
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Proper metric for measuring the similarity between two images
I want to calculate the similarity between these two images:
and
These are brain topography maps and colors inside the circles represent the area being activated while watching TV. Thus I am looking ...
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Binary Classification of Images- CNN
I am learning ML and am working on a CNN problem where I need to classify images of CATS and DOGS.
The way I have setup the labels is that cats are 1 and dogs are 0. I have made the final output layer ...
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Using standard deviation in the calculation of ratio to get the scale of values
In the youtube tutorial (Building makemore Part 3: Activations & Gradients, BatchNorm), standard deviation is always used to calculate ratio between two values (e.g. grad to data ratio, update to ...
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Does clustering belong to the domain of data mining or to the domain of machine learning?
Question 1. Does clustering belong to the domain of data mining or to the domain of machine learning? Or to both domains?
Question 2. Depending on the answer to Question 1, could you please suggest a ...
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Decreasing the summary length from langchain load_summarize_chain?
How can i reduce the output size of the summarization in langchain map reduce method ?
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RMSE of whole and part of test dataset
Can anybody help me to understand the behavior of metrics (RMSE namely) when testing model? I have NN with 1 hidden layer for regression task. RMSE equal 0.07 for external test dataset. But if I break ...
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for multi-tasking training should I use big datasets as one file or each table as one csv?
Assuming we want to create a dataset of books.
There would be dataset A: id_book, title, Author, Subject, Topic, Body [paragraph 1,2,3], Sites ...
I was wondering what if i split each book into one ...
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How to model noisy time series data? Does non-linear modelling help?
Is it possible to model time series data that fluctuates. The main solution is to take first differences and make it easier to fit conventional models. What if non-linear models are built? Can they ...
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Check the validity of the distribution in the proof of No-Free-Lunch Theorem
I'm reading the proof of No-Free-Lunch Theorem (quoted at the end of this question) in Understanding Machine Learning: From Theory to Algorithms, Cambridge University Press, p.37, the author wrote:
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Alternative to ELU and Leaky ReLU?
I was talking with a friend about different activation functions (we are still new to ML). One thing that I didn't like about ELU was the vanishing gradient, and about Leaky ReLU that it's not ...
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Preprocessing overheads in Machine Learning
Meta reports that data preprocessing overheads is fast becoming a bottleneck to machine learning training (https://engineering.fb.com/2022/09/19/ml-applications/data-ingestion-machine-learning-...
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why I got TypeError: linear(): argument 'input' (position 1) must be Tensor, not int in NN?
I am writing a NN in pytorch. I have a list of tensors as input i.e. I created a list Y by appending 1000 tensor vectors(linear tensors) of size 3072. So, each Y[i] is a linear tensor of size 3072. ...
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How to define a DataLoader or Loss for a e.g. multivariable functions?
I am trying to write a NN for estimating a f:R^n --> R^m. My problem is how to train network. I mean if I want to define a dataloader, how to attach X \in R^n to its related Y \in R^m ? Because ...
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Is Repeated K-Fold Cross Validation Enough to Evaluate a Machine Learning Model?
I am training models with a small dataset (around 800 observations) and I am using Repeated K-Fold cross validation to evaluate the models.
Initially, i am using the same cross validation for ...
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Models vs algorithms
What are basic difference between algorithms and models
Is regression SVM random forest decision trees algorithms or model
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Ranking risky routes
i'm looking to get the top 10 rank of the most dangerous routes. I have a routes table where each row is a route and it has features such as
avg daily traffic for past three years
No. of times where ...
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How to know which rules were applied to predict one sample in trained decision tree model?
I have trained Random Forest Regressor from sklearn. I am able to return text representation from each Decision Tree rule using tree.export_text (sklearn documentation here). But it shows rules for ...
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L2 regularisation included in Validation Loss is counter intuitive?
I have been trying to tune hyperparameters for a neural network - I noticed the validation data loss for tensorflow in particular includes the L2 regularisation loss as a measure of the total loss.
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Deep learning model produces very different results when classifying the same samples
I'm trying to design a simple deep learning application for biometric system verification, but every time I run the application I get very different results and I can't figure out why. I don't use ...
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Is there a list of all the incorrectly labelled MNIST images along with their correct labels?
It seems that its well known that the MNIST handwritten digit dataset contains quite a few examples where the labels are clearly wrong and correspond to the wrong digit, some examples of people ...
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How to represent facial features from video and classify high/low personality traits from facial features?
The dataset has 3-minute 30fps video conversations (no audio) of 150 extroverted and 150 introverted individuals. The goal is to classify them as "introverts" or "extroverts" based ...
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Identification of inexactly-recurring material in time series stream
I am working on a personal project involving the analysis of a stream of audio data and the identification of (non-verbatim) repeated subsequences.
My research on time series has so far lead me to ...
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Detecting cropped features/objects in an image
I hope someone can point me in the right direction. (Cross posting from the main SO flow page)
I have some images and I'm trying to write something in Python to detect if the objects in the image have ...
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Why is T5 often used in text-to-data for text prompt encoders?
In the text-to-data(music, image, audio, etc.) generative AI field, one method of encoding text prompts is to use pre-trained language models. Such an approach was used in research on Moûsai [1] and ...
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Text and Checkmarks Extraction from an Image
I am working on a project where in I have a filled form which is a safety inspection checklist and I am processing it through AWS Textract.
I am able to fetch text, layout, tables, signatures but ...
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