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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How to combine recommended lists produced by two different models?

Suppose there are two algorithms that I use to generate recommendations for a user, the first one producing list A, the second one producing list B, both of length $k$. Is there a clever way of ...
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Inference speed of ReLU networks

I'm fairly new in the topic, and I was wondering whether some of you can point to existing works in which the inference of deep neural networks with ReLU activation functions is tested on GPUs as a ...
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Song playlist recommendation system

I want to build a recommender system to suggest similar songs to continue a playlist (similar to what Spotify does by recommending similar songs at the end of a playlist). I want to build two models: ...
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How to suppress "Estimator fit failed. The score on this train-test" warning message?

I am working on hyper-tuning random forest classifier with following parameters in random search CV ...
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Creating synthetic data to increase the training data for ML model

I was wondering if I can create more data from the available dataset to train my model and what are the pitfalls of such a practice? I have a modest number of examples in my dataset, but I believe ...
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What kind of Keras data classification mode should I pick for my kind of machine learning problem?

So I have to come up with a machine learning model for a specific problem that I have. As far as I know, Keras allows for four different types of classification: Binary, Categorical, Multiclass ...
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Explanable AI ! Is someone facing this issue? What are you doing to solve this problem

Is anyone here dealing with the problem of explanable AI? i.e. how are you able to understand and interpret predictions made by your machine learning models. Anyone here facing this problem or already ...
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Deep Learning accuracy vs Confusion Matrix accuracy

I am working on deep learning with fer2013 dataset. After training the model I got val_precision: 0.9168 (precision: 0.8492) ...
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Poor neural net regressive fit to data that exhibit clear structure

I've been trying to use a simple NN to model data I've generated. The data lack a closed form expression, but exhibit clear structure. The MWE below emulates similar data. I find that any NN I create, ...
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Can we extract data types of features/variables from pickled model for Logistic Regression, Decision Tree, Random Forest?

I am trying to extract data types of variables/features from a pickled ML model file. I could see there is no information of the data types of variables in pickle file except for XG Boost. Is there ...
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Feature selection before or after scaling and splitting

Should feature scaling/standardization/normalization be done before or after feature selection, and before or after data splitting? I am confused about the order in which the various pre-processing ...
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Neural Network for solving these linear algebra problems

Intro There are several questions on this site about whether or not machine learning can solve specific problems. The answer (in my words) seems to be: "Yes, trivially, if you choose a model to ...
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Mixed Data Type Classification / Neighbor Algorithm

Here is a hypothetical simplified dataframe of my problem, which would be low dimensional (20ish features), containing some made-up information about certain dog breeds: Breed Min_Weight Max_Weight ...
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How to perform Grid Search on NLP CRF model

I am trying to perform hyperparameter tuning on sklearn_crfsuite.CRF model. When I try to execute below code, it doesn't give any exception but it probably fails to perform fit. And due to which, if I ...
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Kmeans clustering in python - Giving original labels to predicted clusters

I have a dataset with 7 labels in the target variable. X = data.drop('target', axis=1) Y = data['target'] Y.unique() array(['Normal_Weight', 'Overweight_Level_I', '...
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Model a classification problem with multiple categorical varialbes as input features only. Diff Model performance

I'm having an input data with 100k rows, 8 input features, I'm trying to predict y (binary 1/0). But all the X are categorical variables(strictly nominal variables, not ordinal). Some with 8 levels, ...
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Cross Validation after using train-test to decide optimal algorithm to use?

I am interested in training different algorithms on a data set and observing performance metrics. Currently, my approach is to train different algorithms on train data, and then evaluate performance ...
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Maximum entropy optimization for approximating image quality score distribution - as in Google's "Neural Image Assessment" paper

I am asking this question after a thorough research on the internet and having read every single detail of "NIMA: Neural Image Assessment" by Hossein Talebi and Peyman Milanfar. Before ...
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Successive Predictions

I am facing a problem which you could abstractly describe the following: I have a pool of possible customers. I want to know if a customer appears during one year. I want to predict the total revenue ...
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Normalization and Denormalization

I have few queries. 1) Is normalization required for ANN / CNN /LSTM ? 2) If we normalize the data with MinMax Scaler, then in that case how to denormalize it and when to denormalize it so that we ...
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What is custom SVM kernel?

What is custom kernel in the Support Vector Machine. How is it different from Polynomial kernel. How to implement a custom kernel. Can you provide a code to implement a custom kernel.
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What are the disadvantages of accuracy?

I have been reading about evaluating a model with accuracy only and I have found some disadvantages. Among them, I read that it equates all errors. How could this problem be solved? Maybe assigning ...
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Why following functions are NOT reasonable loss functions? Assume we can find the optimal parameters for each loss function

Which of the following functions are NOT reasonable loss functions? Note that is the prediction and y is the true target value. Assume we can find the optimal parameters for each loss function. ...
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How to balance sensitivity(sn) and specificity(sp) of an Artificial Neural Network model?

I have been working on a binary classification problem of protein sequences. I have used a feed-forward neural network with two hidden layers. I have the training and validation accuracy/loss curves ...
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Neural Networks that can mimic Decision Trees

tl;dr: Are there neural networks (specific activation functions, set up of layers ...?) that can mimic fairly well decision trees/if statements? === I am trying to build a model from some simulated ...
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Using VADER Sentiment Analysis makes distributions overlap : how to improve my model

I use VADER Sentiment Analysis on a "customer reviews" dataset. VADER breaks down feelings of satisfaction and dissatisfaction into neutral and positive negative components. Plotting the ...
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How are the confidence intervals of a model interpreted?

I am doing some work with R and after obtaining the confusion matrix I have obtained the following metrics corresponding to a logistic regression: ...
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Problems with KNN using tidymodels

I am analyzing a database and I want to perform a KNN. I am using the 'tidymodels' library and when I run the model, I get the following error: ...
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How to understand CV

How to interpret cross_validation score in that case? While cross-val equeals minus
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How can humans improve machine learning models?

I'm a UX researcher and have begun working on how to improve machine learning models for a new role. One question I have is how data from humans can be useful for improving a machine learning model. ...
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What causes explosion in MSE when training?

I (probably) well overfitted/overtrained a model. But I was just curious as to what might cause this type of behaviour. I carried on training (Epoch 1/50 is not the first epoch of training this model)....
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Activation Function Hyperparameter Optimisation

If I have a model, say: ...
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How do I decide the frequency of data capture for modeling? How does it affect my final model?

I plan to capture data to predict energy consumption in a food processing plant. I want to capture production details such as how much each category of food is produced, what is the machine's output, ...
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Drop Out in Hyperparameter Optimisation

Is it correct to add dropout to each layer and that it is done as in the below example? class MyHyperModel(kt.HyperModel): def build_model(self, hp): ...
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How to define a custom layer in pytorch

I am new to PyTorch and seeking your help regarding a problem I have. I need to add a costume layer to a NN in training phase. Please see the figure which shows a simple DNN with the custom layer. NN ...
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How to predict an outcome of the game (next row) based on all previous games (rows)?

I'm a data science student and I've come across a fairly unusual dataset (to me, which explains the vague title). It's of the following form: STAT_1 STAT_2 ... HOME AWAY NEXT_HOME NEXT_AWAY ...
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Forecast new loans that will be granted next month using machine learning NN regressor

I'm attempting to apply a machine learning regression solution using NN to the following problem: I have the history of loans granted by a bank, and I need to forecast what loans will be opened in the ...
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Should hyperparameter optimisation focus on many trials (models) lower epochs first, then a second round with few models, many epochs?

Rather than a hyperparameter optimisation with kt.tuners.RandomSearch, say, that does (option A), say X model trials (e.g. 100), Y epochs each (say 100, so a total of 10,000 epochs across all models) ...
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Is there some model trained over algebraic finite fields?

I am wondering if at this moment there are machine learning algorithms whose training algorithm is performed over finite fields. You can assume that the training dataset is in some finite field too. ...
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Reinforcement Learning vs Retraining

I have created a complex ML model using supervised learning. For the sake of discussion, let's say my model identifies dogs and a human labels the output as "correct" or "not correct&...
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Best way to get independent rows in your data set?

I have contract data which has some repeats of accounts so some of the rows are dependent on one another. How can I deal with this? I was thinking to put each account's data into a single row, but ...
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Incremental learning on Autoencoder for anomaly detection

I want to incrementally train my pre-trained autoencoder model on data being received every minute. Based on this thread, successive calls to model.fit will incrementally train the model. However, the ...
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Which metric to use to evaluate prediction problem

The product manager wants to know if you can develop a model to predict the number of views a listing will receive based on the boat's features. She would consider using your model if, on average, the ...
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Making a regression problem into a classification followed by regression problem

I have a dataset where the output value ranges from 0-600(greater than 600 is possible but is not present in my dataset). But the number of 0's in my output variable is close to 50%. I've converted it ...
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CodeReview: CycleGAN Implementation Using Keras FunctionalAPI

Okay So I Am Here Implementing the cyclegan architecture with using keras api from scratch. For Those who Wanna Know More About Cyclegan seehere The CycleGan Compose of Two Phase Architecture Like ...
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how to improve recall by retraining a model on its feedback

I am creating a supervised model using sensitive and scarce data. For the sake of discussion, I've simiplified the problem statement by assuming that I'm creating a model for identifying dogs. Let's ...
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Joining of Technical replicates with experimental data

I have a task in which I need to join data collected from non-destructive biological sensor analyses with data collected from various microbiological "wet-lab" methods, e.g. colony counting, ...
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Is there any works in the direction of dimensionally reducing the size of DNNs?

I am talking about a scenario where you first train a "huge" Neural Network and then try to scale it down without sacrificing much of the accuracy. I am not talking about quantization of ...
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Is it possible for a feature not correlated with a dependent variable to become important in a machine learning model?

Is it possible for a feature not correlated (or faintly correlated) with a dependent variable to become important in a machine learning model?
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Text classification with Weka (unlimited dependent variable values)

In our dataset we have 2 attributes, citizen and nric. The rule is if citizen is US, then the result should be the nric value, otherwise Non-US. Could you please suggest which algorithm in Weka I ...
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