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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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21 views

Which statistical test to use in this case?

I’m working with a nassCDS dataset trying to solve a group of exercises of a task in R. In a previous step I create a new column (vehClassif) on the dataset from the continuous variable weight ...
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32 views

Custom RMSE loss not the same as taking the root of built-in Keras MSE loss [closed]

I have defined a custom RMSE loss function: def rmse(y_pred, y_true): return K.sqrt(K.mean(K.square(y_pred - y_true))) I was evaluating it against the mean ...
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17 views

Do I have to meet multiple linear regression assumptions for conducting linear predictions? [duplicate]

I am intending to use python to predict a linear outcome.I am wondering if I need to meet the assumptions (e.g. normal distribution of continuous features) of a linear regression? If so, do all ...
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1answer
22 views

Feature vector formulation for a Neural Network [closed]

So I'm implementing a simple ANN where I have a massive input data set. The input data contains all kinds of stuffs like eg: categorical values: button,table, image...; binary values: true-false...; ...
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14 views

Is it possible to combine both cross validation time serie and stratified

We suppose that I have the following dataset, where: The data describe the behaviors of each employ during the realization of his mission from the first day he starts working on the society. Each ...
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1answer
22 views

Decision trees - hypothesis test for quality of split

I was just introduced to the concept of decision tree. I read that hypothesis testing can be used to asses the quality of each split $$H_0: \text{split was bad}$$ $$H_a: \text{split was good}$$ I ...
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10 views

Finding the Bias and Residual Components of Mallows Cp

So I have this question but am unsure on how to do it: If a true quadratic regression function is E(Y) = 15+20X+3X^2, and the fitted linear regression function is Y_hat=13+40X, for which E(...
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24 views

Linear regression: in practice, how likely is it for $A^\top A$ to be non-invertible and what fundamentally causes it?

The linear regression problem involves solving, $$\|Ax - b\|_2^2$$ where $A$ is the data matrix and $b$ is the target vector. The solution to this problem is known to be, $$x^\star = (A^\top A)^{-...
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24 views

Violation of the IID assumption in Gradient Boosting

Generally, machine learning methods make little to no statistical assumptions. However, a key assumption they do make is that the data are IID. What are the implications of a violation of the ...
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16 views

How to select the most optimal hyperparameter in grid-search cross validation if the process is repeated X (i.e. 3) times?

I have some data (total N = 100,000 rows). I randomly selected 10% from it to become the validation set to help identify the best set of hyperparameters. To do that I am conducting grid search based ...
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1answer
27 views

Can additive boosting be used in case a classification?

In case of a regression we can apply a boosting approach as follows: Train a very simple model using the data set. Find a difference between the predictions and targets and use this difference as a ...
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80 views

How to plot a marginal histogram correctly? Why is my plot different to the one in a textbook?

Chapter 1 of "Machine Learning - A Probabilistic Perspective" by Kevin Patrick Murphy gives this figure (fig_1), and says this is a pairwise scatter plot on iris dataset. The diagonal plots the ...
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1answer
33 views

Fundamental understanding of Gaussian Process and their terminology [closed]

I am new to this site as well as Machine learning, so kindly bear with me. I have been trying to understand Gaussian process and their implementation. Notation: 1) Let's say that the $\vec{x}$ $\in ...
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23 views

Representing and Training Individualized Models

Say I want to create a handwriting OCR or speech-to-text system intended for many users. A first pass might be to train a single one-size-fits-all model on all available data to predict all users' ...
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10 views

Negative Log Likelihood for Censored Data

I'd like to use negative log likelihood as an objective function to model roughly gaussian data with right censoring. My objective function will look something like the following, but I'm not sure how ...
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1answer
46 views

Will Boosting reduce variance?

I've seen two conflicting arguments: In a Stanford cs229 note, the author claims that boosting will increase variance (see section 2.5): http://cs229.stanford.edu/notes/cs229-notes-ensemble.pdf Prof. ...
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12 views

Nearest centroid classifier and normalization

Is normalization useful when using nearest centroid classifier? If yes, should normalization be performed on the data (before computing the centroids) or after learning, on the centroids themselves?
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22 views

Likelihood Matrix from a Random Forest?

I'm going through the supporting material of a paper (https://science.sciencemag.org/content/360/6384/81) , trying to reproduce their results (see below, note that HVG=highly variable gene). The data ...
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33 views

Estimating Required Data for NLP Classification Models

Are there general guidelines for how much data is required for natural language processing (NLP) classification models? I understand this may depend on the text quality, text length, how accurate the ...
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47 views

Binary classification - Assess class imbalance with model performance

I am working on a binary classification project with imbalanced classes. The model has been trained and validated on a 50/50 split. I now want to apply my model on a realistic test set to assess its ...
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1answer
24 views

Genetic Algorithms for Feature Selection

Is anyone able to provide a simple explanation of how genetic algorithms can be used for feature selection in machine learning?
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1answer
31 views

Is this an example of imbalanced data for regression?

I'm new to machine learning, and want to train a regression model based on 1000000 labelled samples (with, say, r features and 1 target). On a histogram, I see that the targets approximately follow a ...
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24 views

Does episodic reinforcement learning still need a discount factor?

The discount factor in reinforcement learning is used to determine how much an agent's decision should be influenced by rewards in the distant future, compared with rewards in the near future. My ...
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14 views

Aggregate data for machine learning. Weights or fake disaggregation?

I have a dataset of medical centers and I need to predict their infection rate, based on the center characteristics and aggregated patient data (eg. percentage of patients which underwent a certain ...
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17 views

How can underfitting and overfitting Classifiers of the same Type perform equally worse?

An rbf support vector classifier is instantiated with different values for C and gamma. Then learning curves are plotted for each of the classifiers. Some classifiers consistently achieve very high ...
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11 views

How to generate a path given a set of input features?

I have set of features for each element of my training set: features = [time, status, speed, on/off, ...]. Such features are just a single value for each element ...
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19 views

Neural Network Inversion and its consequences

I am currently looking at Federated Learning. Here is a good example from google. The idea is that training happens on multiple devices. This means on one hand that training data never leaves a user (...
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21 views

Is it possible to infer overfitting or underfitting from an accuracy vs epoch curve like the one below ? If yes, How?

In this curve, there's a pretty big difference between validation and training accuracy in the end. Does that mean it's overfitting?
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10 views

Does the kernel trick functions represents the Z value of a 2D point that are we going to display it into 3D space?

I am new to machine learning and the support vector machine is one of the hardest to understand in terms of math. Using one of the rbf kernel functions: $$k(xi,xj)...
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12 views

Will historical data lead to target leakage?

I'm bulding a employee churn model. I've employee data from 2016 to 2019 (of people who stayed/left the company), my goal is to train using data from 2016 to 2018 and predict on 2019. Since there's ...
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15 views

Repeated K fold cross validation

I want to use repeated Kfold cross validation in my experiment, since I have a small dataset that might be prone to fluctuation in results of a regular cross valudation regime, so I am opting for a ...
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1answer
31 views

How can we conclude that an optimization algorithm is better than another one for a problem at hand

When we test a new optimization algorithm for a particular problem at hand, what the process that we need to do?For example, do we need to run the algorithm several times, and pick a best performance,...
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36 views

How to prove if h is PAC learnable?

Let $X = \{(x_1, x_2) : 0 \leq x_1 \leq 1, 0 \leq x_2 \leq 1\}$ be the two-dimensional unit square. Let $H_0$ consist of all sets consisting of a finite number of points from $X$ . Let $H$ contain ...
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25 views

Workaround for word embeddings that do not “see” antonyms

Most word embeddings do not "see" antonyms. For instance, among many words they will place vectors for "dependent" and "independent" (as an example) quite close, - actually as close as with synonyms ...
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9 views

What is best way to crossover[Genetic Algorithms]

In Genetic Algorithms there are five phases 1)Initial population 2)Fitness function 3)Selection 4)Crossover 5)Mutation I have solved 2-dimentional problem using this algorithm ...
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3answers
2k views

Neural network vs regression in a small sample

I have a small numeric dataset with 20 observations and 30 variables. I want to approximate Y as a function of the rest 29 Xs(x1,x2,x3...x29). I've tested: neural network (NN) with 1 hidden layer ...
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1answer
17 views

Is it wise to reduce the number of labels in a multi-class classification problem?

I'm working on a dataset which has 5 labels or the outcome, Y. I'm going to use ML model to predict the 5 labels. While doing the data analysis, I found that class1(60%), class2(39%),class3(0.33%), ...
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1answer
39 views

Linear Regression: how to discern a possible correlation between observation errors from scatter plot

The following is excerpted from An Introduction to Statistical Learning by Tibshiriani et. al. In page 66, they introduce the standard error for linear regression coefficients: $$Y = \beta_0+\...
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15 views

chi-square test of the log likelihood ratios for a nested hypothesis testing on 4 models of linear regression

I trying to reconstruct a procedure from an article that I read. First, I had to build 4 models to my raw data (points of X and Y)- one model is constant, second is linear, third is constant and then ...
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1answer
30 views

Nested Cross-Validation on the whole data?

I am performing nested cross-validation, and I know that the idea behind it is to see how the model generalizes. For that, we don't only shuffle the training data but we also do shuffle the testing ...
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1answer
51 views

Variational inference with dependent variables

Classical variational inference uses mean field theory because of its computational benefits, i.e. assumes that the latent variables are independent. For gaussian distribution, it wants to find a ...
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17 views

Neural network for PDE: Should we train the PDE using more initial and boundary data at the beginning?

I was trying to solve a partial differential equation (PDE) using a neural network. The solution to the PDE is not unique unless the boundary condition is determined. In my case, the neural network ...
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16 views

Polynomial regression on uncorrelated features

I'm looking to do polynomial regression on 2 relatively uncorrelated features. Even with very high degrees, I find that the model doesn't fit very well to the data (figure attached here). Since the ...
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2answers
59 views

Why does this paper use training and test sets with different distributions?

From Doruk Cengiz. Seeing beyond the trees: Using machine learning to estimate the impact of minimum wages on affected individuals. 2019. I noticed a strange way of building the training and test ...
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25 views

How to test significance of difference between regression coefficients for multiple interaction categories?

Suppose I have N multiple categories (a discrete interaction variable) and representative samples of which. I'm fitting a multi-variate X linear regression model to response variable Y for each ...
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15 views

Learning to rank for time series with LightGBM data formatting

I have multiple time series, one for each item, say item1, item2,...itemN. N>=500. I have features associated for each of the items and a dependent variable. I am currently studying learning to rank ...
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1answer
57 views

What does it mean to obtain a sample $S$ of size $n$ according to a distribution $D$ over a set $X$ in machine learning?

What does it mean to obtain a sample $S$ of size $n$ according to a distribution $D$ over a set $X$ in machine learning?
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53 views

Linear Regression in Python using gradient descent

(cross-posted from data-science StackExchange)(someone recommended that this community is more appropriate for my problem) I am trying to implement a simple multivariate linear regression model ...
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1answer
13 views

What does it mean when someone says they have sparse labels?

I'm going through the Hands-On Machine Learning book with Scikit-Learn and TF by Aurelion Geron and I've come across the notion of choosing a specific loss function due to the data having sparse ...
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1answer
63 views

Could someone give an concrete example to illustrate how “empirical distribution” relates to “histogram”?

This question is derived from this one, which is related to empirical distribution. I did a little bit search and then got this and this, unfortunately, none of them mentions "histogram". I've ...