Questions tagged [machine-learning]

Machine learning algorithms build a model of the training data. The term "machine learning" is vaguely defined; it includes what is also called statistical learning, reinforcement learning, unsupervised learning, etc. ALWAYS ADD A MORE SPECIFIC TAG.

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Improve the perfomance of the deep learning model based on the train and validate loss curve [duplicate]

I have a deep learning model and the following is the loss on the train and validate data. The prediction for my model is not good. Do you know what I should do for my model to have a better results? ...
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Combining text and numerical in ML model [closed]

Have anyone implemented this approach? https://towardsdatascience.com/combining-numerical-and-text-features-in-deep-neural-networks-e91f0237eea4. If so, mind sharing? I don't understand it. Thanks.
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YOLO v2 loss function

I'm trying to understand (and implement) the YOLOv2 loss function, which is not given explicitly in the original paper. There are several posts on this topic, but quite a few seem to confuse the ...
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Post analysis using raw data or SHAP values in Machine learning

Let's say I have SHAP value returned in dataframe for input variables like below ...
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Usefulness of KS tests and other similar distribution comparing tests

I am working on a machine learning binary classification problem. I have an outcome variable status called as loan paid and <...
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R squared of subgroups

I am trying to predict a value using a linear regression, and I get an R squared of 0.63. My data is composed of 5 different groups (each with different ...
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Should we care about correlations between model performances while we search for the best model?

When we fit (train) a regression model, we usually pick the best performing model (for example the one which gives the smallest RMSE). By doing this we do not take into account the correlation between ...
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Dependent variable standardization in neural networks

I am using a multilayer perceptron model to predict urban temperatures. I have standardized the independent variables before training the model. However, I have not standardized the dependent variable....
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Can a kernel be constructed from any arbitrary bivariate function?

For a kernel $\kappa(x_i, x_j)$ to be considered valid, it must be symmetric and have a positive semidefinite gram matrix for any set of points $\{x_1,...,x_n\}$ (ref: page 4 of http://www.cs.berkeley....
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Significance test for comparing different 10-fold cross-validated Machine Learning Regressions

Is there a recommended significance test for comparing different 10-fold cross validated regressions? For instance, I want to compare the performance of LASSO against Random Forest for my dataset. ...
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Why PR score is down when balanced accuracy is good?

I just read this discussion here and here. I have a dataset of 977 records where class proportion is 77:23. My balanced accuracy is 75.5, ...
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How is AUC helpful when we only need one threshold of a classifier

AUC is a summation of performance at different thresholds, but do we only care about a good performance at one threshold? Imagine a classifier with a low ROC but shots up at point of a low FP and high ...
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Predicting Value in multi-dimension data

I need help with my data, I mix 2 different powder materials to get one powder with a specific quality number. So, this resulted in quality numbers depending on 4 features, the quality of materials 1 ...
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Feature preprocessing (standardize and normalize) and variable independence

I can't find clarity on this question so here goes: Suppose I have 3 features, x, y, z. I know x and ...
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Statistical test when comparing oversampling to no oversampling on ANN

I use 70% of the dataset for training and 30% for testing. I use oversampling on the training dataset with an ANN. I use the test dataset on my ANN and look at the performance of oversampling against ...
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Area Under Precision-Recall and Area Under ROC curve for different amount of observations

I am doing a research and thus comparing some algorithms for binary classification. Worth to mention that, the data set is highly imbalanced i.e., the minority class is only 0.2%. Notation: Area Under ...
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Can somebody help me understand the sentences in more readable expresions?

I was reading a paper about "bayesSimIG" and I have problem in understand the following paragraph.I have read it many times and did a lot of research for it and have understood what each ...
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95% confidence interval for the goodness of fit scores in regression

I see the following computation available online in classification setting. ...
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Are there smooth analytical penalty on leaves sizes for decision trees?

In a decision tree, when we search for an optimal split, we usually minimize root mean square deviation (RMSE). In addition to that we might forbid splits that give to small leaves (for example a leaf ...
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How to force splits in decision tree to be distributed uniformelly in case of no dependency on feature?

I have targets ordered by a feature. I want to find a single split that minimizes a squared deviation (RMSE). For example, I have 100 values (targets) and it might be the case that, if I split them as ...
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Understanding types of LSTM and their use cases

I'm currently considering to use RNN/LSTM for a predictive modelling project that involves time-variant points. From looking at the following types of LSTM/RNN (in the picture below), I want to try ...
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1 vote
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Why am I getting 100% accuracy for SVM, Random-forest Classifier and Logistic Regression?

I'm using an existing disease prediction model to build a chatbot. While I was referring to the model I realized that it has an accuracy of 100%. I'm not quite sure how and why the accuracy is 100%. I'...
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Why is the model getting 100% accuracy for SVM, Random-forest Classifier and Logistic Regression? [closed]

I'm using an existing disease prediction model to build a chatbot. While I was referring to the model I realized that it has an accuracy of 100%. I'm not quite sure how and why the accuracy is 100%. I'...
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Rules, theoreticial basis on selecting which machine learning model acceptable to be combined into a voting classifier

Background: Voting ensembles (hard/maximum voting, averaging/soft voting) and stack models are considered as the ensemble technique that can improve individual performance of the machine learning ...
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2 votes
1 answer
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Image Clustering (Unsupervised learning) on unknow class(guess less than 300)

I have 30000 unlabeled images (each image has only one character), and the content of the images is very simple, basically black lines(a language but not English) and white background. I hope to use ...
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Neural Network $\delta^L$ is very often zero [closed]

I'm building a Neural Network from scratch, in order to understand them better. Problem is that even if I spent several days on it, I can't find a way to have it learn something, not even the XOR ...
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Neural Network not learning [duplicate]

I'm building a Neural Network from scratch, in order to understand them better. Problem is that even if I spent several days on it I can't find a way to have it learn something, not even the XOR ...
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Is there a boundary between statistical and machine learning methods?

I want to better understand what exactly is considered "machine learning" or not. I realize there might not be a categorical answer for this, but that's what I want to understand. ...
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Missing "None" class in outcome variable. Least bad way of handling missingness?

I'm dealing with a preexisting dataset with an outcome variable of suicide which entails the following classes, of which multiple can be selected, but they roughly escalate in severity. Check if ...
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How to fix this ValueError: Shapes (None, None) and (None, 3, 3, 16) are incompatible in VGG16 [closed]

I am currently fine-tuning a VGG16 on a multi-classification problem. The requirement is to add a new 1 Conv block, 1 Maxpool layer, 2 FC layers, and an output layer. I have removed the top layers of ...
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Help! Cluster analysis of curves - grouping already grouped data

I have derived a number curves which illustrate the relationship between two (non-time) variables at different locations. I want to be able to group these curves / functions together, so that ...
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Range for Euclidean distance between two variables [closed]

I have two datasets A and B. I want to compare elements of those datasets to find "matches", i.e., elements from ...
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1 answer
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Will these skills in machine learning be relevant in fall 2023? [closed]

I have a masters in civil engineering but would like to work within IT. From my education I have a mathematical and data analysis background. I'm considering taking a course in machine learning to ...
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Why variable representation plays a role in prediction?

I am working on binary classification using a random forest, where the data have 977 records and 6 columns. The class ratio is 77:23. I have two derived input variables. One variable is called ...
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Precision vs Recall Tradeoff plots 2 separated lines

I'm trying to build a binary classifier with high recall and slightly better precision so as to avoid a lot of False Positives. So far the best scores I have got from all different types of model ...
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Using Inception and FID scores in training?

Is it possible to use the Inception and FID scores in the training of a deep image generation model, i.e. to maximize the scores in a loss function, albeit this is "cheating"? If so, has ...
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4 votes
2 answers
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Stratification of the continuous y (target) variable in regression setting

Is it wise to stratify the continuous y (target) variable when you split your training and testing data from the total sample in regression setting? Here is the approach in python to do implement ...
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Fisher's exact test when resampling from the population

I am using different ML classifiers for making predictions related to a binary classification. I would like to compare two groups. Let's call them A and B. The already trained and tested classifier ...
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2 votes
1 answer
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Should I join train and validation sets for final NN model training? If yes, when to stop training the final model?

Normally we divide our dataset into 3 sets: train set, validation set, test set. We use train set to find optimal parameters (weights and biases of NN) and validation set to find optimal NN ...
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1 vote
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is nrmse scale-dependent?

Im trying to evaluate my regression models using a normalised version of the RMSE, nrmse = rmse(y, y_pred)/rmse(y, y_mean) where y_mean is the array of the same len as y filled with the mean value of ...
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Estimating Top n Prices points on a given day for a particular product which would maximize revenue

Problem Statement :- On any given date of the year, get top n prices which would maximize the revenue for that day for a particular platform. Dataset :- Date Price ($p_{i}$) Platform $X_{1,i}$, $X_{...
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1 vote
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What is a differentiable approximation to the indicator function a != b [closed]

I am dealing with an optimization problem where I'd like to regularize two parameters $a$ and $b$. The penalty should be $1$ if the parameters differ and $0$ if they are the same. The motivation is ...
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Using machine learning model trained on standardized data for real world low volume data

I have developed a machine learning model which has been trained on a preprocessed data by scaling and centering using h2o package of R. I am able to use this model ...
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1 vote
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Balancing Multiple Evaluation Metrics for a Model

When evaluating a machine learning (or other statistical model) against multiple evaluation metrics, is there a standardized way to choose the "best" model? As a concrete example, for a two ...
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Which are well-known/popular feature selection methods? [duplicate]

I am trying to test the performance of first reducing the number of features before applying methods such as neural networks for prediction. Due to the fact that the number of observations is not ...
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1 vote
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How do I perform a train-validation split on data with class imbalance such that the class imbalance ratio is preserved?

My data has class imbalance-- that is, some classes have significantly fewer training samples than the others. I want to perform a train-validation split in such as way that the class ratios are ...
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Finding feature values for regression model such that output is more than a given value?

Suppose you have an (online) shop. You have a dataset containing $p$ features (representing customer characteristics) $X = (x_1, \ldots, x_p)$ and a feature $y$, representing how much money a customer ...
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0 votes
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Implications of Violation of Independence Assumption in ML Models

This is a somewhat broad question, but I'm having trouble finding a good answer anywhere. I know many ML models will impose an independence assumption in the data. But I'm having a hard time really ...
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3 votes
1 answer
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Matrix Factorization and Overfitting

I recently came accross the algorithm of Matrix Factorization for a recommendations system. One of the tutorials I followed can be found here. According to it given the initial matrix $R$ and the ...
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3 votes
1 answer
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Distance between two clusters after their joining in centroid linkage

For a distance between two clusters A and B of objects given by $d_{AB}=\left \|{m_{A}-m_{B}} \right \|^{2}$ , where $m_{A}$ is the mean of the objects in cluster $A$, show that the formula ...
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