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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12 views

Classify new data (unlabelled) with Support Vector Machines

I am starting on Machine Learning and I am having some questions. I took 5 measurements from 500 specimens (known sex) to train a model and see if can successfully assign their sex (I used Support ...
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4answers
305 views

Train and Validation vs. Train, Test, and Validation

I am embarking on a new job that will give me the opportunity to do some cool machine learning stuff. I haven't touched this stuff on a deeper level since graduate school and I wanted to get some ...
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Demonstrating overfitting like Figure 7.1 in Elements of Statistical Learning

Background Elements of Statistical Learning (ESL) has an excellent figure which demonstrates the bias/variance trade off in this figure (7.1 in the book) I would like to recreate this figure for the ...
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1answer
18 views

Cross validation with GridSearchCV or train-val-test split

I have a question regarding the CV in GridSearchCV. To test my model should I split my data into 3: training, validation, test? For easy understanding let's say my data is split into training with 60% ...
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1answer
18 views

Would it be appropriate to compare knn and cnn algorithm for facial recognition?

I wanna compare these two algorithms but I'm not sure whether I should and also what parameters to keep in mind if I do ? what should I consider if I do go forward apart from the same data set of ...
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What parameters are initialized in Sparse Bayesian Learning?

I am reading few papers on Sparse Bayesian Learning topic. Many papers use posterior calculation or approximation using likelihood function and priors. Usually priors are defined like this P(w|Alpha) ...
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What is the marginal log-likelihood in a Multi-Head Model?

I have to study the model described here. Given a passage text and a question about it, the model tries to find the correct answer to the question. This model follows a multi-head approach: each head ...
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11 views

Comparing quality of automatically labeled data

I have two algorithms for automatically labeling data for a computer vision task. The dataset is large, and it's not feasible to inspect visually. Can I determine which labeling is higher quality ...
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27 views

Overfitting very quickly when using SMOTE or ADASYN

I am currently working on a binary time-series classification problem using the keras deep learning library. The dataset that I am working with is heavily ...
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1answer
29 views

What is meant by Expressiveness in neural network?

While studying Batch normalization, I came across the parameter sigma and beta in the output. And all the information said that they are added in order to retain the "expressive power of the ...
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1answer
23 views

braking up multiclass classification

I am working on a data set that has tabled data to 5 different classes. I would like to train an algorithm (logreg most likely) to predict cluster affiliation. but because there are 5 classes all the ...
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How to deal with features encoding and normalization for deployment of ML model?

I have a dataset that has many different features such as categorical, ordinal and continuous ones. Categorical Features I have great difficulty understanding how should I apply label encoding to ...
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What is done in practice to speed up batch computation for deep learning by standard software packages? [migrated]

Something that confuses me with machine learning in practice is that when you send in a batch of examples, you are computing the value of each example independently. And then when you backprop, you ...
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Finding differences between groups of users - and identifying how are they different

Following up on my previous questions I'll start with the basics and hopefully the community could help me find a solution! I have distinct groups of users, let's say they are grouped by geographic ...
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Tensor linearization interview question [closed]

I got the following question in a coding interview for machine learning engineer position. Write a function: ...
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1answer
15 views

When to use which feature selection methods?

I am new to ML and learning about feature selection and reduction. I have a few questions q1) I see there are a lot of feature selection algorithms such as Lasso, <...
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1answer
30 views

How to find bits/dim of a gaussian output distribution?

I have images that are 64x64x3 and 64x64x1 8-bit. I transform those images down to [-1,1]. I now want to find the bits/dim for my VAE log probability. How do I find the bits/dim of the log likelihood? ...
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Why feature selection using `L1` and not using `L2` norm? [duplicate]

I read a tutorial here. In which, I came across the below plots I read an explanation quoted ...
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1answer
97 views

Regression With Uncertainty/Range In Target Variable

I have a question relating to what methods/machine learning techniques exist for modelling when you have uncertainty within your target variable (in regression problems). For example, suppose you have ...
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9 views

How to build an ensemble for different set of features?

I have a dataset in which part of the features have more data than the other part, and for avoiding to build a full data set with a small amount of data, I'd like to build two models, each one ...
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22 views

distribution of image data?

(I don't much about deep learning, but have been playing with a few things and have some questions.) I took the (pretrained on imagenet) resnet18 model from pytorch, removed the last fully-connected ...
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15 views

Is AUC or calibration error comparable among different subsets with different number of samples if I calculate them on subsets of test set?

When we fit a machine learning model, we will use metrics like AUC or calibration error to check the model probability ranking estimate or probability estimate on the test dataset. I tried to ...
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48 views

L1 vs L2 norm - Circle and Diamond [duplicate]

I am new to ML and recently came across the L1 and L2 norm. The tutorials that I read here and here show some circle and diamond ...
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2answers
112 views

Predicting millions of independent time series, using them to help each other

This is a very general problem faced by different types of companies. Predict future customer behavior over time. Imagine that we have 1 million customers with their own resources over time, forming a ...
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1answer
25 views

Derivation of expected loss ESL (integrating over conditional expectation confusion)

I am trying to understand the derivation of expected loss (equation 2.11 in Elements of Statistical learning) and there is a specific step I do not understand. We start with $EPE(f) = E(Y - f(x))^{2}$ ...
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12 views

LDA QDA with constant value of 0 in dataframe

I have an issue with LDA and QDA methods. I noticed that when I use these methods on a dataframe full of dummy variables ( possibility of columns with only with the value of 0) , these methods don't ...
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1answer
47 views

Baffled by cosine similarity - these results seem counterintuitive

Haven't used cosine similarity much in the past so getting into it now. Seeing results that are counterintuitive and would love your help making sense of them. Assume these simple vectors: ...
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114 views

Adding (meaningful) features does not improve model performance

I am struggling with confusion matrices and their outputs. I thought to follow all the steps right, but unfortunately it seems that something is not going well. I had a dataset built and labelled on ...
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28 views

Predict Longitude and Latitude time series

I have a dataset that describes location (latitude longitude) in different times of day. The dataset is for one day and has 10 minute intervals (so 144 observations). I need to predict the location at ...
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8 views

ML algorithms for classifying sequences

The problem I'm trying to solve is that of analyzing a user's behavior (for identification) from data collected through their phones. For example, accelerator, gyroscope, etc. And at any point, I have ...
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66 views

Machine learning using neuroimaging data

Suppose I have collected the hemodynamic responses of participants when they were performing cognitive tasks (e.g. n-back) using a 16-channel functional near-infrared spectroscopy device. I would like ...
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16 views

Silhouette Coefficient acceptable value

Does anybody know about the acceptable values for Silhouette Coefficient (or maybe Calinski-Harabasz and Davies-Bouldin index) in K-means clustering?. I know that Silhouette Coefficients close to 1 ...
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1answer
53 views

Figure out relative importance of entity attributes

I'm trying to understand how various aspects of a movie contribute to its gross revenue. I want to rank a movie's attributes in that sense - the attributes that most strongly determine the revenue are ...
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22 views

Clustering text embeddings: TF-IDF + BERT Sentence Embeddings

I am trying to cluster a few thousand forum posts that are similar in content to Stackoverfow. So far, I have tried two main approaches to represent the posts: TF-IDF Sentence embedding based on BERT....
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1answer
28 views

Cubic Clustering Criterion in Python

Does anybody know if any package calculates the Cubic Clustering Criterion (CCC) index and the Approximate Expected R Square (http://documentation.sas.com/?docsetId=emref&docsetTarget=...
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9 views

How to select a comprehensive set of parameters for Hyper-parameter tuning Extra Trees Regressor / Random Forest Regressor

I'm trying to use as much parameters as I can in hyper-parameter tuning of Extra Trees Regressor and Random Forest Regressor, so I'll be sure on the model I'm going to use. The parameters in Extra ...
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1answer
14 views

Should we apply feature transformation for test data?

I am working on a regression problem. The data contains 13 features (after performing feature selection). to some of these features, I have applied log transformation and box-cox to fix the skewness. ...
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8 views

Validation dataset for a Gaussian process in a problem of time series forecasting

I am relatively new to Machine Learning, which I am trying to study to define a Gaussian process in a problem of time series forecasting. Then, for Gaussian processes, we wish to select a mean ...
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11 views

Statistical benefits of K-fold cross validation

I understand how k-fold cross validation works. For each iteration, the training data is split into $k$ portions, and using $k-1$ portion of the data for training and the $k$-th portion of the data ...
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11 views

Machine learning terminology (method, model, architecture, task, objective)

I got a bit confused about the usage of machine learning terminology in books / papers / discussions that seems somehow not completely consistent to me. Therefore, I want to know if you would agree ...
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1answer
27 views

Does high variance and high bias mean that it is both underfitted and overfitted?

I know that high bias and low variance implies underfitting, while high variance and low bias implies overfitting. But what if both bias and variance are high? Does this mean that the model is ...
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8 views

Statistical methods for landcover classification

For example, if I have this dataset where I’m taking per year pland values so the coverage of a habitat per 5x5 modis cell – And for a specific ID, where ID represent each unique 5x5 modis cell (the ...
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13 views

When do I use correlation and entropy for Feature Selection?

I am wondering when to use correlation and entropy to select features from the dataset. I understand correlation and it is used to see how correlated two variables are and it is only used between two ...
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16 views

How to determine the sample size for evaluating a machine learning model based on Mean absolute percentage error?

I have recently implemented a Regression machine learning algorithm that predicts house prices. We have defined Mean absolute error as an intuitive metric to calculate how much off each prediction can ...
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18 views

different numeric value of the same category in Target Statistics of CatBoost

Here is the famous paper for CatBoost: CatBoost: unbiased boosting with categorical features https://arxiv.org/pdf/1706.09516....
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1answer
25 views

In which cases should I split the data in training set and test set [closed]

I am taking a course on machine learning and in one problem I should perform a Ridge regression to fit some given data to a known model. I was wondering if, in this case, there are any advantage in ...
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1answer
30 views

Equality of optimization problems of the soft-margin SVM (moving constraint to objective function)

I'm reading the derivation of the soft-margin SVM optimization problem in Elements of Statistical learning. In it, the authors claim that \begin{align} \min_{} \quad & \| \mathbf{w}\| \\ \...
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Is there a general theorem about NP-hardness of training neural networks?

It is often stated that training a neural network is "well-known" to be NP-hard. Looking through the literature the often quoted papers are 1 and [2]. [1] proves the NP-hardness for finding ...
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1answer
26 views

How to make a decision - when there is a tie and no human expert

We have two algorithms (simple rule-based) working on labeling the dataset as "Yes" and "No" for a disease. There is no ML involved in this task. For ex: If Algo 1 says subject 1 ...
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34 views

Using LIME without intercept term?

I'm playing with LIME to explain the prediction of a machine learning model. LIME trains a (locally weighted) linear surrogate model around a point of interest. The weights of that surrogate model are ...

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