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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Plotting decision boundary of logistic regression on unscaled features

Context I recently started learning ML algorithms, So I started implementing Logistic Regression for learning purpose. I implemented Logistic Regression based on Dr. Andrew Ng's lectures. Github link ...
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Elements of Statistical Learning - Statistical Decision Theory : step 2.10 to 2.11 minimization of EPE

Questions about that section of the book (see image below) were already asked and they cover how to get from 2.9 to 2.11, see here and from 2.12 to 2.13, see here but do not cover how to get from 2.11 ...
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OVerfitting using Random Forest - classification

I have a dataframe which is a made of many datasets combined together (many datasets with the same predictive features but with different samples combined together). This dataframe, called ...
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Understanding and combining feature selection and cross validation for random forest

Suppose we are interested in the random forest classifer and the hyperparameters are n_estimators:[100,500,1000], max_depth: [1,5,15] This gives rise to 9 ...
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1 vote
1 answer
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Coordinates of the lasso estimator

The lasso estimator is $$ \hat\beta = \underset{\beta}{\text{argmin}}||Y-X\beta||_2^2+r||\beta||_1 $$ I always read that the coordinates $\hat\beta_j$ of the lasso estimator tend to be either clearly ...
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Textbook on high-dimensional statistics

I am a beginning PhD student in biostatistics and want to learn about high-dimensional statistics. I have looked into the books by Buehlmann/Geer, Wainwright, and Giraud, but they seem to be targeted ...
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Does an increasing-and-then-decreasing validation loss mean the model is overfitting

I'm currently using an EEGNet model to train on baseline corrected EEG data (containing 64 channels sampled at 512 Hz) to classify three mental states hence it's a 3 class classification problem. The ...
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How to weigh time-series data with a utility function with jumps?

I have data measured at fixed intervals in one process. It needs to be weighed with cost value that is generated in another process that is analogue and the costs it generates have jumps. Imagine a ...
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Calculating mutual Information in absence of a token/string over a class

Equation: MI(X,Ci) = ∑P(X,Ci) . log ( P(X,Ci) / P(X)⋅P(Ci) ) where, X is url, t is a url token and Ci is the i-th class will be either spam or ham P(t,C) will be either the frequency that the word &...
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3 votes
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what is the idea behind SHRINKAGE (regularization) METHOD (e.g LASSO), and how to interpret it?

I've read ALOT of papers and questions here as well, on other websites, but I didn't get the point behind the LASSO. my background is in economics (not an expert in math). sorry for my bad english... ...
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multivariate vs multiple time series - term explanation and forecasting model

Here is the time series I have: (p1s is an abbreviation of product 1 sales in dollars) ...
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1 answer
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Not improvement in representation of encoder in AE

I am trying to train an autoencoder on tabular data containing categorical data. After training AE, I use the encoder for classification. I normalize numerical data and use one-hot encoding for ...
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Classifying dataset with different number of features

I have a dataset like below: samplename position reference alternative S1 201 C T S1 3567 A G S1 760 T C S2 356 C T S2 6787 T C These data belongs to patients and ...
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2 votes
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RFM Customer Segmentation - Normalizing data for using different clustering algorithms

I am segmenting customers using the RFM technique. The dataset looks like this: Sample Recency shows the number of years since the last payment (ranging between 0 and 4). In this context, using years ...
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8 votes
5 answers
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Success Stories of "Statistics"? [closed]

When it comes to Machine Learning, the successful application seem to be very well known. For example, Neural Networks have been successfully used to create Self Driving Cars and Game Playing ...
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Handling new customers in customer propensity model

I'm using last four years' data to predict whether they will buy or not buy in the next quarter. One problem I'm facing is customers who are not four years old. Is it right to keep them in the data ...
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Can I use batchnorm in CNN + RNN, and where to place it exactly?

I have designed a following neural network that combines CNN, RNN and Dense layers. It aims to predict a positive or negative outcome for the time step t+1, given a ...
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How to improve the max( 0, 100*(metrics.r2_score(actual , predicted)) score ..?

I am a newbie to machine learning, I am working on the identifying the habitability score of the property, I have data like this, ...
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Neural Network Feeding - Custom Information Extraction

I am trying to train a neural net. to extract specific information from text. I need to find entity information like attribute, dependency, etc. The text input will be like this: ...
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evaluating smote performance

I have a multi-class problem with highly imbalanced data. The 1st and 2nd classes are 4-5x greater than the 3rd class. Since I am planning to run predictive models, I am using smote technique to up ...
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Determining what lift can be attributed to tiered rewards program

Statistically, is there a way to attribute the sales increase from tiered rewards program? First off, I feel like I know this answer to this (I need more data), but I’m new to stats and thought you ...
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Predict certain values in time series (not next time step)

I have input of numbers as -5,-4..4,5 I only want to predict which value will appear next first 5 or -5, sometime in the furure, not necessarily in the next step How should I define my model?
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How to find statistical significance of predictions made by one model in two different test sets

I have trained a support vector machine and a random forest classifier to make predictions in a certain period. Thereafter, I have excluded a certain period from the original test data to see how the ...
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1 answer
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How to properly impute values on the test set using imputer (missForest)

I'm trying to impute some missing values on my dataset $X$. So first I shuffle and split data to obatin the train set X_train and the test set ...
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Relation between generalization bounds of Kernel Ridge Regression and largest eigenvalue of the kernel Gram matrix

Consider a positive-definite, symmetric function $k(x_1, x_2)$ which is used, given the dataset $\{(x_i, y_i)\}_{i=1}^m$, to construct the Gram matrix $K = [k(x_i, x_j)]_{i,j \in 1, ..., m}$. What is ...
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How to use a tuned Random Forest Regressor (post validation and testing) to forecast the future values for a univariate time series?

I have a three-year univariate time series dataset with weekly data entries. I have split the series into a training set (2 years of data) and a testing set (data from the final year). I have trained ...
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How to compute the weighted class probability estimates in SAMME.R?

Does anybody know how to compute the weighted class probability estimates in the SAMME.R algorithm in [1]? I have created a simple example dataset as shown in the following. A decision tree with depth ...
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High validation accuracy and training accuracy but low test accuracy

I have a LSTM model that has good training accuracy(~90%) and excellent validation accuracy(> 95%) but it gives poor results when I test it on data it hasn't seen. I am training hyperparameters ...
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2 votes
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Why use IsolationForest over other supervised methods for semi supervised learning?

I have a dataset with labels that I'm using to explore unsupervised learning (IsolationForest) with. IsolationForest has a few hyperparameters, and some can be heuristically determined like maybe you ...
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2 votes
1 answer
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Can a variable be linearly independent, but non-linearly dependent?

I am reviewing a friend's paper, and they are throwing out variables that are below a certain correlation coefficient value before doing a multiple linear regression model. Is this a wise thing to do? ...
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Write down the likelihood [closed]

Can someone help me with this question?
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1 vote
1 answer
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interpreting confusion matrix results

I have a dataset on unemployed individuals enrolled in a job training program where I am trying to predict whether 6 months post-enrolment they 1) gain employment, 2) stay unemployed, or 3) drop out ...
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2 votes
2 answers
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Can a regularization harm more than help in the situation of a huge over-fit?

I fit a regression model on a data set and get some in-sample RMSE. I wanted to know, how likely is that I get this good RMSE (or even better) under assumptions that there are no patterns in the data. ...
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Why am I getting a lot of 0 predictions in collaborative filtering using Alternating Least Squares? [closed]

Model: Using ALS model in Pyspark Data: Here the userCol are sellers and itemCol are products sold by them. The ...
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1 vote
1 answer
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How should I train my CNN with a tiny dataset

I'm working on a problem where I aim to classify sections of a track made on the floor using tape, into either left turns, right turns or straight track. I'm struggling creating a CNN that is not ...
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4 votes
1 answer
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XGBoost when P>>N

Someone built an XGBoost classification model using each pixel in an image (256*256) as a separate feature, plus a few other features. However they only have 500 data points. The target classes were ...
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Does dataset sparsity affect rate of convergence of model solution?

I have a model with not too many ordinal data. The model performs at a 90% accuracy. I am thinking of adding 13 ordinal variables and transforming them using one hot encoder. The transformation will ...
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About the paper "Deep Unsupervised Learning using Nonequilibrium Thermodynamics"

I have spent some time studying the paper Deep Unsupervised Learning using Nonequilibrium Thermodynamics. At page 5, the authors discuss the following integral: $$\int d\mathbf{x}^{(1\cdots T)}q(\...
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Scatter Plot of the Ranks of spearman vs Hoeffding [closed]

Could anyone tell me why the answer is AccountBal
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Is there any definition of oversampling in regression? [closed]

Is there any definition of oversampling in regression? like the ratio : 90:10; 80:20; 70:30; 50:50
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6 votes
1 answer
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Do Statistical Binning Algorithms Exist?

I am sure most of us have had this problem at some point. Take for example the random forest algorithm and its implementation in R. This algorithm can not handle a categorical variable with more than (...
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1 vote
1 answer
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Why do we regularize large gradients corresponding to large errors?

While reviewing some scientific blogs, I found them recommending using gradient clipping for large error gradients. However, intuitively one would think that when model predictions are completely off, ...
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1 vote
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How to implement simple VAE with sparse tensor in Tensorflow

thank you for reading. I have been attempting to train a simple VAE on very sparse 2D and 3D data. So far I have been training using dense tensors which - I think - is resulting in horrible training ...
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computing the "mix effect" in the evolution of a variable on R

Background: I am currently working on a dataset representing the evolution of the income of a hospital which hosts several medical specialties. The ratio income/medical act increases between year $N$ ...
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How is the basic formula of a transposed convolution the opposite of a regular convolutional neural network?

I am trying to create an FCN from scratch in c++. The "ending" part of an FCN is fully connected layers, and transposed convolutions. I have a working CNN ...
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2 votes
2 answers
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Best way to obtain probabilities and model explanations with imbalanced data

I am currently working on machine learning problem with the following characteristics:  - Data have binary outcomes and are severely imbalanced (positive class is ~0.5% of my sample of ~500,000 data ...
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Why is tensorflow with shap values only explain one feature on my dataset

I have a one dimensional convolutional network defined with tensorflow but now after I trained the model and done with all the evaluations I must explain feature contribution using SHAP Values. ...
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Is Modelling of an Under-determined System possible?

We have a dataset with around 20,000 variables and only 200 observations. Our Naive Modelling: We split it into train set (=150 observations) and validation set (=50 observations) and fit Linear ...
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How to check whether the KMeans clustering have the appropriate labels for each group?

I am doing a Kaggle customer segmentation clustering problem and my current results or labels have quite strange problems: In one label group, the customers who have a high spending did not have a ...
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0 votes
1 answer
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Validation dataset is not a random sample from whole dataset

I am facing a small dataset to do machine learning. The small dataset is not the dataset on which ML will be applied. It is a derived dataset on which the model will be trained. However, due to ...
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