Questions tagged [train]

training (or estimation) of statistical models or algorithms.

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CV.glmnet results [duplicate]

I was working through the lab on ridge regression and LASSO in ISLR and I came across a strange behavior in the cv.glmnet function. When I followed the lab as ...
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1answer
4k views

What is the best strategy to train and validate classification using PLS-[classifier] in caret package?

I have 4 clusters (see plot below) extracted from data of medical samples N=218 measured for 11 genes/predictors P=11 by this ...
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1answer
122 views

Linear regression from data that don't represent a function

I have $(x,\ y)$ pairs with a strongly suspected linear correlation. So I want to fit the "best" linear function in order to make predictions for unknown $x$'s. These pairs don't represent a function, ...
3
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1answer
1k views

Data split into training and test

I am implementing an EEG classifier with 15 subjects (patients), specifically a support vector machine classifier. I randomly choose the training and testing sets, but I was faced by a question "how ...
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0answers
1k views

Metric warning using caret's rfe

I am using the caret package to do feature selection with rfe while training a knn ...
2
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2answers
431 views

Is it always bad to retrain your model to include predicted data?

I understand intuitively why this is a horrible idea - you assume your model is correct and then increase your number of observations which will likely result in a poor fit on future data. I'm ...
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2answers
71 views

Is “model selection” the same as traning?

A terminology problem. In machine learning we have the following problem: Choosing the optimal model (or training): $$ f^* = \arg\min_{f \in \mathcal{F}} \sum_i l(x_i,y_i) $$ Is the term "model ...
1
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1answer
4k views

R caret difference between ROC curve and accuracy for classification

In case of caret package test function metric option, one can use either accuracy or ROC as a metric that will be used to finalize values of tuning parameters. I felt that accuracy and ROC are the ...
4
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1answer
6k views

LGOCV caret package R

i am learning data mining through book . During classification chapters about Neural Networks the authors have below code. I have below questions: ...
4
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3answers
3k views

Text Annotation tools

I have tried open nlp NER for extracting organization names with not great success (It could be model is not fit for the domain I am working). So, I am planning to train Open NLP NER on my training ...
3
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1answer
786 views

Feature selection in the training set

I have a classifier, and I am using leave one out cross-validation to assess its performance. On each iteration, I divide the dataset into training and testing sets. The testing set is just the ...
3
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1answer
1k views

Methods for time-series prediction depending on multiple parameters

We have hourly time-series data of the status of a system: number of people present at different train stations. We collected it for a year, and we want to use it to train a model to predict the ...
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0answers
247 views

Forming training set for Multinomial Naive Bayes

Is it true that Multinomial Naive Bayes requires equally by count training data for each class to get best performance? For example, we forming classifier for three classes - Japan, China, Korea. ...
2
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2answers
508 views

Combining labeled and unlabeled data for training

When training my algorithm, if I can get some i.e. data my future test data that has no labels can it improve my algorithm's efficiency, is there any mathematical proof for it? PS: I think semi-...
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1answer
1k views

Logistic Regression Cost Function issue in Matlab

I'm trying to implement a logistic regression function in matlab. I calculated the theta values, linear regression cost function is converging and then I use those parameters in logistic regression ...
3
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1answer
846 views

SVM retrain on whole dataset for final model --> overfitting?

i am training a SVM (RBF kernel) with a dataset of ~1500 samples (balanced) using fminsearch on the CV error for parameter optimization (C and s). After i found the "best" parameters (local optima ...
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1answer
749 views

What is Generalization errror on training set. How can I see it on weka?

I get output like this .. ...
7
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1answer
4k views

Can a neural network output represent a posterior probability?

I seem to remember from years ago when I first read Bishop's ANN book that it is possible to construct a neural network such that the outputs should represent the posterior probability that I would ...
8
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6answers
10k views

Is using the same data for feature selection and cross-validation biased or not?

We have a small dataset (about 250 samples * 100 features) on which we want to build a binary classifier after selecting the best feature subset. Lets say that we partition the data into: Training, ...
1
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1answer
1k views

Machine Learning and training time: is it really relevant?

I have a question regarding the time needed for training a classifier. I am facing the specific problem of Sentiment Analysis (classification of text as pos/neg/neu). (Excepting online learning ...
10
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3answers
434 views

Approaches when learning from huge datasets?

Basically, there are two common ways to learn against huge datasets (when you're confronted by time/space restrictions): Cheating :) - use just a "manageable" subset for training. The loss of ...

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