Questions tagged [machine-learning]

Methods and principles of building "computer systems that automatically improve with experience."

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How are Q, K, and V Vectors Trained in a Transformer Self-Attention?

I am new to transformers, so this may be a silly question, but I was reading about transformers and how they use attention, and it involves the usage of three special vectors. Most articles say that ...
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structuring time dependent features classification

I have a binary classification task at hand to predict Status = 1,0. The independent variables are a function of time, with each row representing a task that updates the status if things change . in ...
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Differentiable loss function for ranking problem in regression model

In regression problem, we may need a loss function to measure the relative ranking accuracy between targets $y$ and predicted values $y_{pred}$. Abviously, the simple MSE does not consider such ...
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How to re-train a model from false positives

I'm still a bit new to deep learning. What I'm still struggling, is what is the best practice in re-training a good model over time? I've trained a deep model for my binary classification problem (...
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How to predict an outcome within a specific time window?

I have a dataset which has around 10K records. My objective is to predict whether the customer will churn or not. Binary classification problem with each class representing around 55:45 proportion ...
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Multi-class clasification

Just getting my toes wet with running some models on the Iris dataset and was wondering if using One-vs-Rest is required or not? Because I can fit a linear model without it, but using OVR yields ...
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Why is it giving me error of “Expected 2D array, got 1D array instead” [migrated]

I used regressor.fit([X_train], [Y_train]), it did worked but when I ran the below code ,it gave me the following error "ValueError: shapes (1,9) and (21,21) not aligned: 9 (dim 1) != 21 (dim 0)" ...
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Hindsight Experience Replay (HER) results obtained 50 times faster than original paper?

I am reproducing the results from Hindsight Experience Replay by Andrychowicz et. al. In the original paper they present the results below, where the agent is trained for 200 epochs. 200 epochs * 800 ...
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Generalised Estimating Equation (GEE) vs. Recurrent Neural Network (RNN)

Has anyone looked into or know what is the difference between a GEE model and an RNN model in terms of what these two models are doing? Apart from the differences in structure of these two models ...
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artificial neural network for vehicular speed prediction based on volume

I an trying to develop a neural network for vehicular speed prediction. my question is how can i train neural network with many input output combination of data?
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Is this scheme correct for logistic regression with stochastic gradient descent

I am implementing logistic regression with stochastic gradient descent, but it is not working as expected. I've tried many epochs and different learning rates $\alpha$ but the probability of belonging ...
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What does $\mathbf{w^Tx}+w_0$ graphically mean in the discriminant function?

I found a post explaining the discriminant function very detailed. But I am still confused about the function $g(\mathbf{x})=\mathbf{w^Tx}+w_0$ in 9.2 Linear Discriminant Functions and Decision ...
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buying a gpu with at last 1200 usd dollars [closed]

i know it may be not an academic question but I used to use google collab for training my model but now I want to buy a good GPU for my self to train deep learning models so what do you recommend to ...
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Unsupervised classification of satellite images sequences derived from time series with SOFM in python?

I have the following data: Up to 2 images per day (time series from 2015 - 2019 with gaps) for a specific region (AOI - Germany - Hesse) with 2 variables (soil moisture, precipitation). Out of this ...
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Python experiment results directory structure creation/manipulation package

so we do numerous types of ML experiments using a number of frameworks. We've ended up writing an awful lot of boilerplate : read some configuration as to the experiment's parameters, variables, ...
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Potential speedup by applying PCA once on dataset with m rows vs. IncrementalPCA to x batches of size m/x?

I've been working on trying to perform dimensionality reduction on high-dimensional, high-volume datasets (with many rows and columns - around 100,000 - 1M rows and ~500 columns). While the size of ...
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K in the Naming of Models and Techniques

Why is k chosen for the names k-nearest neighbors and k-fold cross validation? Is it arbitrary or just another mysterious naming?
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1answer
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How do I know the best pruning criteria for decision trees?

Right now,I am working on decision trees on python,how do I know what would be the best pruning criteria based on my data?
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Cost function in ANN converges to 0.5 and the values of outputs all converge to 0

I have written a simple ANN to understand its internal structure better. However for the past few days I could not understand why it does not perform in the expected way. The way I defined COST ...
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is it ok to get 100% accuracy in random forest classifier algorithm?

while i was building the model to predict the performance of machine using the features like OEF,working time,performance/head etc... I splitted the training data using ...
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1answer
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Does the Koalas library allow to use all Pandas machine learning libraries like Scikit-Learn, XGBoost, and TensorFlow?

I would like to implement a model based on some cleaned and prepared data set. I already have a bit of experience with PySpark, but from a data scientist's perspective it can be cumbersome to work ...
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1answer
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Memory efficient encoding logic for group categories

I have a huge dataset with categorical data. It is comprised of alerts having multiple properties. Each alert belongs to a group, and some even belong to multiple groups. It looks somewhat like this: ...
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understading the corr heatmap

I want your help to analyze this correlation heat map if you look at this heat map what can you inference and what can you apply ?
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Getting big losses and little accuracy on image classification model with cnn

i am currently working on image classification of artworks from this site https://www.kaggle.com/ikarus777/best-artworks-of-all-time and following the tutorial from this site https://...
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Is it possible to convert Neural Network code in Python into Matlab code?

I want to convert the code written in Python into Matlab code. May I know is it possible to do that. Share the available ways or methods to do the conversion. May I know is there any Online ...
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Plotting Gradient Descent in 3d - Contour Plots

I have generated 3 parameters along with the cost function. I have the $\theta$ lists and the cost list of 100 values from the 100 iterations. I would like to plot the last 2 parameters against cost ...
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Extract relevant features from time series data

I have a time series data set from a sensor and the task is to predict the time before a failure event is occurred. The data set has one feature and has almost 20 million rows. This is a regression ...
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ensemble learning with code [closed]

I am trying to create a stacked model using ANN or RNN for NLP as my accuracy is coming 76.5% so I need a python code of stacked ANN or RNN.
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Predicting future period of a specific length with a DNN

I want to predict future the revenue of customers one year in advance with a DNN using transaction data. The issue I'm trying to wrap my head around is how to tell the model that I want the output to ...
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1answer
20 views

How can you adjust a prediction based on features in the future being different than predicted?

I have a model that takes mostly cumulative data, and makes a prediction. It's not baseball, but I'm using this as a pretty accurate analogy. You put in all the totals so far, and it make a prediction ...
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27 views

How to calculate the final adjusted weights for a neural network model

My understanding of a neural network algorithm is the 1st row/observation of the dataset is inputted into the NN model and then backpropagation happens to adjust the weights, until some condition is ...
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72 views

How to train my model efficiently?

I am new to ML and have been reading online about training bottlenecks when there are frequent updates to data. Let's say I have a built a model based on a dataset of 10M records. Now, in another 2 ...
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predict an array like [1,2,3] increasing

I want to generate a simple model and classify it with decision tree. The idea is if numbers in an array are increasing then what I need is that. eg. ...
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Post-classification after inference in deep learning models

I designed a fire detection using Deep Learning binary classification in Keras (fire vs none). It's a simple model with a few layers. In my training dataset, I ...
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Where can I find a dataset with labelled articles by topic? [migrated]

I am looking for a dataset containing articles (with article-text, or alternatively I am fine with only URLs too) and the corresponding topic label (i.e. politics, art, gardening etc.) Any idea?
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K-Means Clustering too crowded

I have written a simple python code that opens a csv files and then clusters the values of one column. There around 10k rows This is my code ...
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1answer
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Creating pronunciation dictionary for ASR

I am working on ASR(automatic speech recoginition) on Somali data as master thesis and now I am stuck with how to create a phonetics or pronunciation dictionary for it. I searched over net and could ...
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1answer
26 views

How to label images for CNN use as classifier

I have theorical question that I couldnt decide how to approach. I have tons of grayscaled shape pictures and my goal is seperate these images to good printed and bad printed. For this, I look at ...
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Transductive Multilabel Classification

I'm trying to use transductive (semi-supervised) multilabel classification on my dataset since I have a low volume of labelled data samples, compared to the unlabelled samples. I found a promising ...
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How can the influence of unknown samples on a model be determined that were not used for training?

I am searching for the correct term or research area: For an already trained supervised model, more precisely a classifier, it should be calculated or estimated, which influence a not yet seen sample ...
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Real-time or offline Data Augmentation for segmenting microscopy images?

I'm doing semantic segmentation(for cells) using microscopy images. I'm exploring U-net and FCN DenseNets for the task. In the U-net paper the authors have trained their model only from 30 images but ...
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1answer
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is validation and train set should be all different files?

Let say I have train set and validation set if 'A' included in the train set. 'A' should not be include in the validation set? or some is ok?
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What is an appropriate approach to sampling for probability of default using a classification model?

If we have a loan book and want to train the data to predict the probability of default, what is an appropriate way to sample the historical data to train the model, given that each account is open ...
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is final fit with X,y or X_train , y_train?

I split the dataset with X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) and the fit ...
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15 views

Clarification of image reverting

I am a newbie to deep learning. Could someone please explain why (img)*0.5 + 0.5 line and img = img*255 will be there for ...
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How to handle time series missing values

I have a database of thermal consumption of 100 buildings. Each file has two columns, one is timestamp and the other is usage. My task is to build a prediction model for forecasting the usage for the ...
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1answer
34 views

predicting next jobtitle

I have a dataset of which has 30M rows each like [current_jobtitles, nextjobtitles]. ...
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1answer
35 views

Unable to make accurate predictions?

I have a dataset of diabetes patients and I am trying to predict the next blood glucose level. I have attached an image below and I have about 1600 records in that csv file containing data of 10 ...
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Machine Learning - How to predict set of fixed fields based on past features

I have quite a large dataset (> 100k rows), which contains information for logistical shipments. (export shipments) The dataset looks like this: ...

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