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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13 views

Help interpreting image prediction

Could someone tell me why my predicted result ("Predicted Heatmap") has "ghost layers" and gray background? What can I do to improve my model? **What I've done to the images ** ...
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Pearson Residual Goodness-of-fit test (Updated)

I am running a logistic regression predictive model with death (sta) as the binary outcome variable, and age (continuous variable), and cancer status (variable can; categorical variable) as predictors....
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1answer
37 views

What kind of ML model can be used to generate/impute unspecified inputs from trained data? [closed]

Context Given a complete data set with fields like, (FirstName, LastName, Sex, DateOfBirth, HairColor, EyeColor, Height, Weight, Location, Occupation, Pet), that ...
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1answer
29 views

Can I calibrate to 100% of my sample in ML regression?

I have a standard ML regression model trained on 80% of my data with 20% saved for testing. I want my model to match my full sample as best possible. Can I multiply my outputs by mean(observations ...
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2answers
305 views

Need help with understanding Decision Trees [closed]

I am struggling to understand how decision trees work. I understand that you need to calculate the Gini coefficients for the sample features and that's how leaves are chosen. My issue is that I don't ...
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24 views

Feed-Forward ReLU Networks as a matrix multiplication

When reading papers, Feed-Forward NN are often formalized as follows: $$\Phi(x) := \sigma(W_L\cdots \sigma(W_2\cdot \sigma(W_1x))\cdots) $$ i.e., the ReLU activation function $\sigma$ applied to the ...
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9 views

How to improve accuracy of natural's Naive Bayes classifier?

Note: I'm new to Machine Learning and NLP. This is my first project in this field. I'm using NaturalNode/natural (https://github.com/NaturalNode/natural) to build a chat bot to help my users with ...
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3 views

Masked language models: can you train on remasked data?

Masked language models like BERT and friends are trained on the task of predicting words removed from input text. Normally, this text is removed at random from some training data. As far as I can tell ...
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How much taking uncorrelated features for a Machine Learning problem is important?

I imagine that taking correlated features for some kind of ML algorithms is no useful (as for a Linear Regression) but it doesn't not hold for all algorithm in general. There are algorithms that don't ...
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19 views

One class SVM and centered data

I understand that the one class SVM try to separate the normal training data point from the origin. My guess is that, if we centered the data in a normalisation step, the OCSMV will works poorly since ...
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1answer
34 views

Cross validation and hyperparameter tuning workflow

After reading a lot of articles on cross validation, I am now confused. I know that cross validation is used to get an estimate of model performance and is used to select the best algorithm out of ...
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1answer
25 views

Evaluating Probabilistic/Bayesian Forecasts - PIT Values & How to Generate

Suppose you are modelling a linear regression $y_i = \alpha + \beta x_i + \epsilon_i$, in probabilistic terms: $$ \mu_i = \alpha + \beta x_i, $$ $$ y_i \sim \mathcal{N}(\mu_i, \sigma). $$ For each ...
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How to interpret the variable importance varImp() when training a LASSO/Ridge regression using the library caret and method = "glmnet"?

I have trained an elastic net regularized model and left with my top two variables - both factors. • How can I interpret the importance of each one? • Should I train a new linear model including only ...
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23 views

Explainable AI - Traditional ML algos

In my work, I mostly use traditional algorithms such as Logistic regression, Linear regression, SVM, Naive Bayes, Random Forests, Decision Trees and Boosting etc to analyze data and make predictions. ...
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7 views

When do we need to log transform predictors for logistic regression model? [duplicate]

I am interested in knowing when we should log-transform the predictors for the logistic regression model? My predictors are highly skewed but I read about some materials online that we don't need to ...
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The Realizability Assumption [closed]

I'm reading the the first chapter of Understanding machine learning from theory to algorithms: I do not understand the meaning of the following assumption: definition 2.1 (The Realizability Assumption)...
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15 views

Finding causal Inference from sentiment analysis

I am conducting a sentiment analysis on thousands of social media posts by unemployed manufacturing workers to see how online sentiment of the group members I am analyzing has changed after an ...
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1answer
66 views

How many predictors can I include in my logistic regression model

If I am dealing with a small sample size (n = 48; n = 29 have disease vs n = 19 without disease), what are the maximum numbers of the predictors I can include in my multivariable logistic regression ...
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7 views

How to determine what predicted probability to use in the Risk stratification table?

I am new to the prediction model and would be very grateful for the advice regarding the likelihood ratio test. I want to construct a risk stratification table like the one in this study (see Table 4)....
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19 views

Understanding machine learning models posted on GitHub [closed]

Recently I've been starting to get into machine learning and I realized that in order to get into understanding more complicated machine learning models I'd need to first look at the seminal papers ...
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9 views

Study Resources for optimizing(increasing/decreasing) a certain feature using AI

I am looking for resources that talk about how we can optimize(increase/decrease) a certain independent variable using other independent variables and the dependent variable. For example, we have 3 ...
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0answers
13 views

Using ground-truth values to update predictions of trained model

I have a geospatial machine learning problem involving training a machine learning model to learn to predict the value of ground truth data from satellite images. What I am hoping to do is be able to ...
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13 views

Making predictions with limited user generated data

We've trained a ML model and deployed it to production. The trained ML model uses about 50-60 features. A user inputs set of information on our platform which is nowhere close to all the features that ...
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3answers
492 views

How come model prediction accuracy high but model does not generalise well

I have trained a couple of models which I'm experimenting with. One is Logistic Regression and the other Random Forest. I've got 10s of 1000s of samples in my dataset (which has 4 features) and I've ...
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0answers
9 views

2NN classifier with neighbours having different labels

In 2-Nearest-Neighbourhood classifier, given a input x, and 2 neighbours of x have different labels, what can I assign as label for x? (Assuming I am not weighting neighbours based on distance)?
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1answer
21 views

K-nearest-neighbor - relationship between K, parameters and complexity

I want to understand the relationship (if any) between parameters and hyperparameters in a k-nearest-neighbor (KNN) model and how they relate to complexity. Assume A model which should classify ...
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0answers
19 views

$R^2$ score, and uncorrelated independent and dependent variables

Considering that the dataset has been cleaned and preprocessed, can a lack of correlation between the independent and dependent variables result in a negative $R^2$ score? A negative $R^2$ score ...
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1answer
31 views

Relation between AUROC and threshold

As I understand, AUROC tells us the probability the model will score a randomly chosen positive class higher than a randomly chosen negative class. Meaning that, if AUROC = 0.7, than we expect that ...
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0answers
15 views

Use X1 to predict Y, the correlation is 0.1, use X2 the correlation is 0.2, what is the range of correlation if combine X1 and X2

For linear regression, if use X1 alone to predict Y, the correlation is 0.1, use X2 the correlation is 0.2, what is the range of correlation if combine X1 and X2. Assume that X1 and X2 are independent....
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0answers
7 views

Can you generate output for ICE plot?

I am looking for multi-step forecast deterministically with a random forest. I am aware the random forest isn't the best model for multi-step forecasting however I need to do it for comparison reasons....
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1answer
41 views

Bayesian Regression: Loss Function Explained

Considering a simple linear regression model e.g. $y_i = \alpha + \beta x_i + \epsilon $ , in probabilistic terms: $$ \mu_i = \alpha + \beta x_i $$ $$ y_i \sim \mathcal{N}(\mu_i, \sigma) $$ We assume ...
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0answers
5 views

What are some strategies to deal with label sparsity when training a protein function prediction model?

The protein function prediction task requires you to take a sequence of amino acids (think words in a sentence, but if there are only 20 words), and output the functions that protein can take. There ...
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11 views

SelectKBest selects my y (target) as a feature. How do I prevent this? [closed]

I'm trying to use Scikit's SelectKBest features, but it keeps picking my target variable/y as one of the 7 features. This is my code: ...
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0answers
25 views

When do Efficient Decision Algorithms for 1D Anomaly Detection exist (compared to threshold-tests)?

I'm tasked with investigating whether machine learning algorithms can be used to efficiently identify if a certain type of anomaly is present in the temporal spacing of incoming network packets. ...
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0answers
9 views

One response variable with multiple categories VS Multiple binary response variables

I'm working on a project and my main dataset consists of approximately 140 independent variables and one response variable with 5 levels/categories. I have an unseen testing dataset and my objective ...
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0answers
21 views

Why doesn't my xgboost model learn? [closed]

What does it mean when your evaluation metric gets worse with every boosting round? For my evaluation metric down is bad. Even if I use learning rates as low as 1e-4, 1e-5, or 1e-6 it just goes down ...
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0answers
14 views

Relation between the lattice points in ROC plot and different pairs of positive and negative classes

Suppose you have a classification problem and you get the following scores from your hypothesis: \begin{bmatrix} 0.87 & 0.30 & 0.40 & 0.10 & 0.23 & 0.70 & 0.90 & 0.60 \end{...
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1answer
35 views

Model accuracy versus F1

When training a model (classifier) in TensorFlow, an accuracy value is returned. What is the interpretation of an accuracy of, say, ...
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0answers
8 views

how do I perform permutation testinging for a prediction model developed within caret package (R)?

I'm fairly new to data science/StackExchange, so please excuse any faux pas I'm trying to perform permutation testing for a chosen ML algorithm (an elastic-net logistic regression) to derive a p-value....
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1answer
11 views

forward and reverse KL divergence for variational inference

I have a question regarding the forward or reverse KL divergence used in variational inference. In accordance with the following lecture notes, reverse KL can cause q under-estimate the support of p ...
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0answers
41 views

Does Amazon use python for its recommendation system or does it use C C++? [closed]

My friend and had a discussion on how a recommendaion system is imbibed onto a website like amazon and had a debate on if they used python or C and C++. We wanted to know how it was done.
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20 views

Subsampling For Class Imbalances and no-information rate

Question has to do with the interpretation of output of the caret package . Subsampling (either up or down) is set up in caret ...
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2answers
36 views

Hold-Out VS Cross-Validation - R caret

I have a question regarding hold-out vs. cross-validation. I have a dataset with ~650 cases which I am analyzing in R using the caret package. There I have a regression problem and a classification ...
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1answer
25 views

Simple Gradient Descent Project plausibility

I am currently in a numerical analysis class at my university and wanted to tackle a project applying gradient descent. Fair warning: I am new to machine learning, but my professor believed in me, so ...
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0answers
6 views

Oscillation of AdaBoost Training error

Adaboost, using weak learners as Gaussian Naive bayes, has oscillating/unpredictable training error as we increase the number of weak learners. Is there a specific reason for this? Y-axis is the ...
3
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1answer
48 views

Are there any mathematical reasons that describe why "sparse models" are desirable?

I am interested in better learning about why Model Sparsity (i.e. Regularization) "works" - whether this is more due to mathematical principles or empirical results (on a case by case basis,...
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0answers
15 views

Values overshooting for sparse matrix factorization (recommendation system)

Using this article as reference for ease of replicability, I noticed that when expanding the pivot matrix R with many missing values, the final recommendation matrix tends to have values overshooting ...
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1answer
21 views

Ensemble Model using Stacking

I learned that building an ensemble model using stacking is done by training a meta-model on the predictions of $n$ other models in order to combine the predictions and try to enhance the performance. ...
2
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1answer
63 views

How can I reduce the propagation of errors in multi-step time series forecasting?

I have a multi-step forecasting task where I am predicting values $H$ hours in the future. Supposing that the forecast issue is done at time t, I will produce predictions for the next $H$ hours: $\{\...
2
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2answers
26 views

Which predictive models output the posterior distribution?

In a supervised learning context, the posterior distribution of the target given the predictors is often discussed in foundational treatments of the subject. One way this comes up is in decision ...

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