Questions tagged [machine-learning]

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

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21 views

How to impute using simple imputer (custom function)

I am imputing my data using simple imputer from sklearn. i want to test many different ways of applying transformations to the data. i.e for logisitcic regression i would like to remove nans and ...
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108 views

Training the document page layout and classifying good/bad layouts

I have a use case where I am supposed to get the coordinates of each block element in a page (whether its paragraph, image, table) where I train a model to understand how they are placed in a given ...
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Relationship between two continuous variables in time series data

I have a dataset that collects daily data based on transactions between two entities. I wish to find the strength, direction, and kind of relationship between two continuous variables i.e. Number of ...
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1answer
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What is the difference between ensemble methods and hybrid methods, or is there none?

I have the feeling that these terms often are used as synonyms for one another, however they have the same goal, namely increasing prediction accuracy by combining different algorithms. My question ...
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Variational Autoencoders(VAE)-zero variance problem

So, i'm having a problem with training my VAE. I'm not sure if i'm dealing with a bug in code or a bug in logic/understanding of the topic. Here is an image showing latent variable variances on test ...
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How to evaluate the performance of a model in production when labeling data is costly?

I have come to a problem for which I can't find a solution. Let's talk about a hypothetical binary classification problem in which you have some years of (human) labeled data. The final objective is ...
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2answers
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Machine learning on classifying speech

So, I have 9k of 1 second wav files of a person speaking. These are labeled by whether the person speaking is wearing a face mask or not. I am supposed to come up with a machine learning model to ...
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1answer
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Why is my LSTM is working best with batch size of 2 and no hidden layers?

I am building an LSTM for price prediction using Keras. I am using Bayesian optimization to find the right hyperparameters. With ...
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16 views

VAE generates bad images. due to unbalanced loss functions?

I'm training a variational autoencoder on CelebA dataset using TensorFlow.keras The problem I'm facing is that the generated images are not diverse enough and look bad. example: What I think: it'...
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2answers
302 views

Combining Classifiers with different Precision and Recall values

Suppose I have two binary classifiers, A and B. Both are trained on the same set of data, and produce predictions on a different (but same for both classifiers) set of data. The precision for A is ...
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Consecutive Feature Selection-CV and Model Selection-CV

I want to ask a question about general workflow of algorithm development. I want to include a "feature selection with Random Forest" step into my workflow but I have doubts about data leakage. It is ...
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What is the best possible method/methods to determine best possible branch(rule) in a decision tree plot for the positive cases only?

I have a dataset, which I am using for loan prediction. Thus, it is pretty much clear that my dataset is imbalanced. I have used Decision Tree to plot the tree structure. Now, I want to find the ...
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1answer
201 views

Data Augmentation Multi Outputs

This question is asked several times here on SE, but I havent been able to find the right answer. I'm trying to build a network with 1 input and 2 outputs. I don't have a lot of data so I would like ...
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104 views

Getting confusion matrix with Keras flow_from_directory

For a homework I have to analyse a set of images. For this I plan to use convolutional neural network. The images are split onto specific folders : A test set with 624 photos dataset/test/normal (...
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2answers
105 views

update model prediction for each day closer to the event

I want to predict whether people will renew their yearly subscription. I want to make this prediction though for each user on every day of their subscription up until the day before the subscription ...
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200 views

Choice of objective function for log transformed data for feedforward NN

I am processing some data using a feedforward neural network in Keras. I have noticed that if I log transform the data, the model trains better, however the error metric on the transformed data doesn'...
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Regression error increase after shuffing data

I'm trying to do multivariate regression using a 3-dimension data set. I noticed a strange problem that my fitting error increase dramatically after I pre-shuffled the data matrix comparing using ...
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76 views

What advantage does Guassian kernel have than any other kernels, such as linear kernel, polynomial kernel and so on?

Guassian kernel is so important in SVM as we know. The parameter gamma is designed for this kind of kernel. My question is what makes Guassian kernel so unique? ...
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Determine how each feature contribute to XGBoost Classification

so for a summary of what I have done: My dataset has 5 classes and 10 parameters. I used XGBclassifer from sklearn to investigate if I could use those 10 parameters to predict the class of each data ...
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1answer
9 views

What are some of the available methods for handling multi-label classification for longer sequences of text

I am looking to solve a multi-class classification problem with long sequences of text with some rows having 1000's of tokens. Some of the state of the art methods such as BERT have a token limit and ...
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3answers
58 views

Problem in implementing CNN

I am using the fashion MNIST dataset to try to work this out: I am using the data from the links: Training : http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-images-idx3-ubyte.gz ...
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Using Keras fit_generator for functional keras models and custom dataset

I have to fit a model that takes three discrete inputs and produces two discrete outputs using a generator made as follows: ...
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6 views

Gamma objective function XGBoost

I am using XGBoost to predict a variable that is highly skewed and always is greater than zero. I did a significant search to see some materials for gamma objective function in XGBoost but I could not ...
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Splitting a 10 year long time-series into multiple year time-series on Deep Learning Models

I'm using recent Deep Learning models for time series analysis such as DeepAR[1] and DeepFactors[2] for my masters. My target time series was given to me by a cement factory, 10 years of compositions ...
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1answer
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Clustering Weekday Weekend Data and Multicollinearity

Hi I have data of weekday and weekend step counts in which I extracted metrics from them such as the wd steps, we steps, standard deviation of wd steps, standard deviation of we steps and so on... <...
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8 views

Batch Normalization as input layer to learn an optimal scaling?

We all know that Batch Normalization reduces "Internal Covariance Shift" and therefore helps Neural Networks to train faster (Batch Normalization: Accelerating Deep Network Training by Reducing ...
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1answer
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Will one hot encoding / unbalanced columns cause bias to Clustering Analysis?

I'm wondering if having too many columns about one certain feature is gonna cause bias to the clustering analysis. For example, if my dataset has columns = ['incoming calls', 'outgoing calls', '...
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Disparity between training and testing errors with deep learning: the bias-variance tradeoff and model selection

I am developing a convolutional neural network and have a dataset with 13,000 datapoints that is split 80%/10%/10% train/validation/test. In tuning the model architecture, I found the following, after ...
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1answer
21 views

Neural Net Backprop Weight updating Pseudo code help please

Here is my code for Backpropagation weight updating. It's a simple network with 1 hidden layer and 1 output neuron. The activation function of both hidden and output layer uses tanh. I propagate the ...
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1answer
62 views

Behavioural data required to predict churn

I am trying to build a predictive churn model that will identify customers who are likely to churn. I am defining a churned user as someone who hasn't transacted within 60 days. 90% of all ...
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Using sklearn's make_pipeline output doesn't match between test dataframe and output dataframe

I have a simple sklearn pipeline defined as below and I create a train_test split to fit and test my model. The R2-score looks ...
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2answers
79 views

How can we create an label, value detector?

I am trying to implement an text detector using MaskRCNN such that the model detects the label and value as shown in the image below. Detecting the same is easier for fields like page date and order ...
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0answers
12 views

Xavier initialisation vs He initialisation

After reading the famous paper, Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification, I understand two things:- He initilization borrows on the benefits of ...
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1answer
18 views

Should dimensionality reduction be done before k-means clustering if there are many features?

My data contains over 200 features and over 500 observations. I want to place the observations into a number of clusters based on the features that make them different. There are numerous ideas I ...
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2answers
56 views

Detect malicious GIFs

I was reading this article talking about a form of targeted internet bullying which involves sending flashing images via Twitter to people with epilepsy. I was wondering whether there is a way to ...
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1answer
44 views

When to stop showing content on recommendation engines?

Let's take an example. I log into my Netflix account and see that it's suggesting the show Friends to me. But I have no interest in watching Friends. So I ignore it. The next time I login, it suggests ...
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2answers
910 views

Alternatives to linear activation function in regression tasks to limit the output

I want to know whether there is a way to limit the output of a regression deep model. Suppose that I want my model outputs values which are in a specified range and penalizes the outputs which are not ...
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11 views

How to deal with images with textual noise?

I have a dataset of images collected from google and bing images (scraped). basically I want to classify these images into binary classes (positive, negative). Images that contain a text originally ...
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1answer
55 views

should I shift a dataset to use it for Time series regression with RNN/LSTM?

I'm seeing this tutorial to know how to use LSTM to predict time series data and I noticed that he shifted the target/labels up so that the features are all in time t but the target is t+1 so my ...
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1answer
134 views

Null predictions for ALS in Pyspark

I am trying to read from my dataset which has three coloumns. (User, Repository and Number of Stars) In[10] ...
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16 views

Can a classifier be trained with reinforcement learning without access to single classification results?

Question: Can a classifier be trained with reinforcement learning without access to single classification results? I want to train a classifier (e.g. Random Forest) using reinforcement learning. ...
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Why i am getting this error? [closed]

I am trying to apply NaiveBayes algorithm on covid19 data sample but this give me a error, if you have any solution kindly share with me
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20 views

Normalizing dependent feature by one of the independent ones

I have a data set with three different features (x1, x2, x3) and I am going to use a regression model to predict y based on the features. x3 is the total amount of money that a customer invest and y ...
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2answers
1k views

Machine learning without explicit training

Is there any set of machine learning algorithms that do not require training and directly gives answers if a set of labeled and unlabeled data gave at once? Is skipping explicit training of model can ...
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1answer
20 views

WHY or WHEN to convert numeric data to a categorical data?

This is an open ended WHY TO or WHEN TO question rather than a question on HOW TO encode numeric to categorical data. I am currently working on Telco Customer Churn dataset from kaggle. This is ...
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10 views

back propagation through time derivation issue

I read several posts about BPTT for RNN, but I am actually a bit confused about one step in the derivation. Given $$h_t=f(b+Wh_{t-1}+Ux_t)$$ when we compute $\frac{\partial h_t}{\partial W}$, does ...
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1answer
45 views

Doubt in Derivation of Backpropagation

I was going through the derivation of backpropagation algorithm provided in this document (adding just for reference). I have doubt at one specific point in this derivation. The derivation goes as ...
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0answers
15 views

Derivation of Bayes classifier in Murphy's book

I am reading Kevin Murphy's Machine Learning book (MLAPP, 1st printing) and want to know how he got the expression for the Bayes classifier using minimization of the posterior expected loss. He wrote ...
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31 views

Maximum Entropy Policy Gradient Derivation

I am reading through the paper on Reinforcement Learning and Control as Probabilistic Inference: Tutorial and Review by Sergey Levine. The link to the paper is https://arxiv.org/pdf/1805.00909. I am ...
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2answers
36 views

Statistical learning for data-limited systems

I'm currently conducting a review for quantitative methods being used for tropical inland fisheries. One of the major problems for modeling methods in tropical inland fisheries is the lack of data ...

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